October 28–31, 2019
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Speakers

Hear from innovative programmers, talented managers, and senior developers who are doing amazing things with TensorFlow and machine learning. More speakers will be announced; please check back for updates.

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Alasdair Allan is a director at Babilim Light Industries and a scientist, author, hacker, maker, and journalist. An expert on the internet of things and sensor systems, he’s famous for hacking hotel radios, deploying mesh networked sensors through the Moscone Center during Google I/O, and for being behind one of the first big mobile privacy scandals when, back in 2011, he revealed that Apple’s iPhone was tracking user location constantly. He has written eight books, and writes regularly for Hackster.io, Hackaday, and other outlets. A former astronomer, he also built a peer-to-peer autonomous telescope network that detected what was, at the time, the most distant object ever discovered.

Presentations

Measuring embedded machine learning Session

The future of machine learning is on the edge and on small, embedded devices that can run for a year or more on a single coin-cell battery. Alasdair Allan dives deep into how using deep learning can be very energy efficient and allows you to make sense of sensor data in real time.

Currently leads TensorFlow model optimization, aimed at making machine learning more efficient to deploy and execute. He is a co-founder and engineering lead of TensorFlow Lite. Prior to that he developed the framework used to execute embedded ML models for Google’s speech recognition software (now in TensorFlow Lite) and lead the development of the latest iteration of the “hey Google” hotword recognizer.

Before Google, Raziel co-designed and implemented the Self-Assembling Interface Layer that forms the core of Appian’s (APPN) low-code development platform.

He graduated summa cum laude from both the B.S. and master’s programs in computer science and machine learning at Mexico’s ITESM.

Presentations

TensorFlow model optimization: Quantization and pruning Session

Raziel Alverez walks you through best current practices and future directions in core TensorFlow technology.

Axel Antoniotti is a staff software engineer at Criteo. His work focuses on developing the platforms and tools that are used by all of Criteo to create any kind of machine learning model, train them, serve them online, and monitor their behavior. He holds an engineering master’s degree from EPITA, a French Grande Ecole specialized in computer science.

Presentations

How Criteo optimized and sped up its TensorFlow models by 10x and served them under 5 ms Session

Criteo real-time bidding of ad spaces requires its TensorFlow (TF) models to make online predictions in less than 5 ms. Nicolas Kowalski and Axel Antoniotti explain why Criteo moved away from high-level APIs and rewrote its models from scratch, reimplementing cross-features and hashing functions using low-level TF operations, in order to factorize as much as possible all TF nodes in its model.

Leonardo Apolonio is a machine learning engineer at Clarabridge, where he solves natural language processing (NLP) tasks, like detecting emotion, call reason, and expressed effort in the customer experience domain. He has experience maintaining and improving NLP pipelines to extract entities and topics from over 30 million websites daily, using the latest NLP and deep learning techniques. Leonardo has also built scalable analytics techniques for anomaly detection using datasets with billions of events.

Presentations

Enterprise AF solution for text classification (using BERT) Tutorial

Leonardo Apolonio takes a deep dive into BERT and how you can use BERT to solve problems.

Josh Baer is the machine learning platform lead at Spotify, building out the tools, processing, and infrastructure for robust ML experiences; enabling teams to leverage ML and AI sustainably in their products, research, and services; and providing a cohesive experience. Previously, Josh led the Hadoop and stream processing teams.

Presentations

Personalizing the infinite jukebox: ML and the TensorFlow ecosystem at Spotify Session

Josh Baer and Keshi Dai take a deep dive into the background of Spotify's historical use of ML and explore how the introduction of TensorFlow and TensorFlow Extended in particular has standardized its ML workflows and improved its ability to bring ML-powered products to its users.

Paige Bailey is a TensorFlow developer advocate at Google.

Presentations

Swift for TensorFlow Session

Paige Bailey and Brennan Saeta walk you through Swift for TensorFlow, a next-generation machine learning platform that leverages innovations like first-class differentiable programming to seamlessly integrate deep neural networks with traditional AI algorithms and general purpose software development.

Joana Carrasqueira is a Developer Relations Program Manager for TensorFlow at Google Brain, where she focuses on bringing together subject matter experts in order to build an open source community around TensorFlow. Prior to Google, she worked on innovation consulting at the Silicon Valley Innovation Center and managed the education department at the International Pharmaceutical Federation for the United Nations. During this time, Joana helped developing new healthcare policies in more than 12 countries and co-authored the WHO guidelines on antimicrobial resistance.
Joana’s research has been published in various scientific journals and she holds a Master in Pharmaceutical Sciences and an Executive MBA from IE Business School.

Presentations

Getting involved in the TensorFlow community Session

Joana Carrasqueira and Nicole Pang explain how you can be a part of the growing TensorFlow ecosystem and become a contributor through code, documentation, education, and community leadership.

Aashish Bhateja is a senior program manager working on Microsoft Azure Machine Learning—building an exciting machine learning service that makes it easy for all data scientists and ML engineers to create and deploy robust, scalable, and highly available machine learning web services in the cloud.

Presentations

Hands-on deep learning with TensorFlow 2.0 and Azure 2-Day Training

Maxim Lukiyanov, Aashish Bhateja, Jordan Edwards, and Mehrnoosh Samekihow explore how AzureML helps data scientists be more productive when working through developing TensorFlow models for production. You'll see the whole model development lifecycle from training through deployment, ML ops, and all the way to model interpretability.

Joe Bowser is a senior computer scientist at Adobe, where he’s the lead developer on the sensei on device team that’s deploying machine learning technologies into various products at Adobe. Previously, he was the creator of PhoneGap for Android and the longest contributing committer to the PhoneGap and Apache Cordova projects. When he’s not contributing to open source at Adobe, he spends his spare time working on various hardware projects, most of which involve first-person-view miniquadcopters.

Presentations

Working with TensorFlow Lite on Android with C++ Session

There are many cases where developers on mobile write lower-level C++ code for their Android applications using the Android NDK, OpenCV and other technologies. Joe Bowser explores how to use TensorFlow Lite (TF Lite) with an existing C++ code base on Android by using the Android NDK and the TF Lite build tree.

Paris Buttfield-Addison is a cofounder of Secret Lab, a game development studio based in beautiful Hobart, Australia. Secret Lab builds games and game development tools, including the multi-award-winning ABC Play School iPad games, the BAFTA- and IGF-winning Night in the Woods, the Qantas airlines Joey Playbox games, and the Yarn Spinner narrative game framework. Previously, Paris was a mobile product manager for Meebo (acquired by Google). Paris particularly enjoys game design, statistics, blockchain, machine learning, and human-centered technology. He researches and writes technical books on mobile and game development (more than 20 so far) for O’Reilly and is writing Practical AI with Swift and Head First Swift. He holds a degree in medieval history and a PhD in computing. You can find him on Twitter as @parisba.

Presentations

Swift for TensorFlow in 3 hours Tutorial

Mars Geldard, Tim Nugent, and Paris Buttfield-Addison say you're wrong if you think Swift is just for app developers. Swift for TensorFlow provides the power of TensorFlow with all the advantages of Python (and complete access to Python libraries) and Swift—the safe, fast, incredibly capable open source programming language; Swift for TensorFlow is the perfect way to learn deep learning and Swift.

Andy Chamberlain is a project manager in the Theoretical Ecology Lab at Stanford University. He specializes in GIS analysis, drone operations, and machine learning.

Presentations

Building deep learning applications using TensorFlow to combat schistosomiasis Session

Schistosomiasis is a debilitating parasitic disease that affects more than 250 million people worldwide. Zac Yung-Chun Liu, Andy Chamberlin, Susanne Sokolow, Giulio De Leo, and Ton Ngo detail how to build and deploy deep learning applications to detect disease transmission hotspots, make interventions more efficient and scalable, and help governments and stakeholders make data-driven decisions.

Wen-Heng (Jack) Chung is a PMTS software development engineer at AMD, where he’s been working on the ROCm stack since its early inception. He has experience in compiler frontend, optimization passes, and run time for high-level languages. His focus has been TensorFlow XLA.

Presentations

Modular convolution considered beneficial Session

Jack Chung, Chao Liu, and Daniel Lowell explore breaking convolution algorithms into modular pieces to be better fused with graph compilers such as accelerated linear algebra (XLA).

Joseph Paul Cohen is a postdoctoral fellow with Yoshua Bengio at Mila and the University of Montreal. Joseph leads the medical research group at Mila, focusing on computer vision, genomics, and clinical data. He holds a PhD in computer science and machine learning from the University of Massachusetts Boston. His research interests include healthcare, bioinformatics, machine learning, computer vision, ad hoc networking, and cybersecurity. Joseph received a US National Science Foundation Graduate Fellowship as well as an IVADO Postdoctoral Fellowship. He’s the director of the Institute for Reproducible Research, which is dedicated to improving the process of scientific research using technology.

Presentations

TensorFlow.js: Bringing machine learning to JavaScript Keynote

JavaScript is the most widely used programming language in the world, and with TensorFlow.js, you can bring the power of TensorFlow and machine learning to your JavaScript application. Sandeep Gupta and Joseph Paul Cohen introduce the TensorFlow.js library and display the amazing possibilities of combining machine learning with JavaScript-based web, mobile, and server-side applications.

Unlocking the power of machine learning for your JavaScript applications with TensorFlow Session

Kangyi Zhang, Brijesh Krishnaswami, and Joseph Paul Cohen take a deep dive into the TensorFlow.js ecosystem: how to bring an existing machine learning model into your JavaScript (JS) app, retrain the model with your data, and go beyond the browser to other JS platforms with live demos of models and featured apps (WeChat virtual plugin from L’Oréal and a radiology diagnostic tool from Mila).

Robert Crowe is a data scientist and TensorFlow addict at Google. Robert has a passion for helping developers quickly learn what they need to be productive. He’s used TensorFlow since the very early days and is excited about how it’s evolving quickly to become even better than it already is. Previously, Robert led software engineering teams for large and small companies, always focusing on clean, elegant solutions to well-defined needs. In his spare time, Robert sails, surfs occasionally, and raises a family.

Presentations

ML in production: Getting started with TensorFlow Extended (TFX) Tutorial

Putting together an ML production pipeline for training, deploying, and maintaining ML and deep learning applications is much more than just training a model. Robert Crowe takes a deep dive into what's involved in creating a production ML pipeline and walks you through working code.

Wisdom d’Almeida is a research intern at MILA, working with Yoshua Bengio and Alain Tapp on fundamental machine learning questions related to consciousness. Wisdom’s other research interests include grounded language learning and AI explainability. He worked on natural language understanding for common-sense reasoning—with application to areas such as healthcare—his master’s dissertation was about medical report generation with natural language explanations. Wisdom’s works in AI won a Government of India National Award in 2018. Previously, he interned at Google in San Francisco and demoed at Google Cloud Next 2018. He holds a master’s degree from KIIT in India and a BS from Université de Lomé in Togo where he grew up. In his spare time, you can see Wisdom struggling with his vocal cords and his guitar strings.

Presentations

Diagnose and explain: Neural X-Ray diagnosis with visual and textual evidence Session

Wisdom d'Almeida walks you through how to design an encoder-decoder model that takes a chest X-ray image as input and generates a radiology report with visual and textual explanations for interpretability. The model was designed with TensorFlow, trained on cloud TPUs, and deployed in the browser with TensorFlow.js. And you'll see a live demo of the model in action.

Keshi Dai is a machine learning engineer at Spotify, working to build out ML infrastructure that supports hundreds of engineers and the growth of ML in products at Spotify. Previously, Keshi worked on the other side of ML as one of the engineers building out recommendation products at Spotify. He knows firsthand the challenges presented when productionizing ML and the benefit in using standard infrastructure in many parts of the workflow.

Presentations

Personalizing the infinite jukebox: ML and the TensorFlow ecosystem at Spotify Session

Josh Baer and Keshi Dai take a deep dive into the background of Spotify's historical use of ML and explore how the introduction of TensorFlow and TensorFlow Extended in particular has standardized its ML workflows and improved its ability to bring ML-powered products to its users.

Jules S. Damji is an Apache Spark community and developer advocate at Databricks. He’s a hands-on developer with over 20 years of experience. Previously, he worked at leading companies such as Sun Microsystems, Netscape, @Home, LoudCloud/Opsware, Verisign, ProQuest, and Hortonworks, building large-scale distributed systems. He holds a BSc and MSc in computer science and MA in political advocacy and communication from Oregon State University, the California State University, and Johns Hopkins University, respectively.

Presentations

How to track and manage TensorFlow 2.0 and Keras model experiments with MLflow Session

Jules Damji explains the MLflow open source platform to manage the model lifecycle. It supports many model flavors, such as MLeap, MLlib, scikit-learn, PyTorch, TensorFlow, and Keras, with particular focus on TensorFlow 2.0 and Keras models.

Shajan Dasan is a staff machine learning engineer at Twitter, where he works on prediction service, enabling different services to perform high-scale inference. Previously, he built distributed systems for information retrieval (web crawler and indexer for Bing), data storage (video, photo, and large-object store at Twitter), and video transcoding (video backend at Twitter), and he worked on the first version of C# language, where he implemented the type safety verifier.

Presentations

Reliable, high-scale TensorFlow inference pipelines at Twitter Session

Twitter heavily relies on Scala and the Java Virtual Machine (JVM) and contains a lot of expertise knowledge. Shajan Dasan and Briac Marcatté walk you through the problems Twitter has had to overcome to make its offering reliable and to provide a reliable TensorFlow inference to Twitter customer teams, and they explain Twitter's key insights.

Giulio De Leo is a theoretical ecologist by formation. He’s interested in investigating factors and processes driving the dynamics of natural and harvested populations and in understanding how to use this knowledge to inform practical management. He’s the scientific director of the newly established Center for Disease Ecology, Health, and the Environment at Stanford.

Presentations

Building deep learning applications using TensorFlow to combat schistosomiasis Session

Schistosomiasis is a debilitating parasitic disease that affects more than 250 million people worldwide. Zac Yung-Chun Liu, Andy Chamberlin, Susanne Sokolow, Giulio De Leo, and Ton Ngo detail how to build and deploy deep learning applications to detect disease transmission hotspots, make interventions more efficient and scalable, and help governments and stakeholders make data-driven decisions.

Jeff Dean is a Google senior fellow in Google’s Research Group, where he cofounded and leads the Google Brain team, Google’s deep learning and artificial intelligence research team. He and his collaborators are working on systems for speech recognition, computer vision, language understanding, and various other machine learning tasks. During his time at Google, Jeff has codesigned and implemented many generations of Google’s crawling, indexing, and query serving systems, major pieces of Google’s initial advertising and AdSense for content systems, and Google’s distributed computing infrastructure, including the MapReduce, BigTable and Spanner systems, protocol buffers, LevelDB, systems infrastructure for statistical machine translation, and a variety of internal and external libraries and developer tools. Jeff is a fellow of the ACM and the AAAS, a member of the US National Academy of Engineering, and a recipient of the ACM-Infosys Foundation Award in the Computing Sciences. He holds a PhD in computer science from the University of Washington, where he worked with Craig Chambers on whole-program optimization techniques for object-oriented languages, and a BS in computer science and economics from the University of Minnesota.

Presentations

Opening keynote Keynote

Jeff Dean explains why Google originally open-sourced TensorFlow almost four years ago. Join in to learn about TensorFlow's progress and how it can solve the problems you care about.

Victor Dibia is a research engineer at Cloudera’s Fast Forward Labs where his work focuses on prototyping state-of-the-art machine learning algorithms and advising clients. He’s passionate about community work and serves as a Google Developer Expert in machine learning. Previously, he was a research staff member at the IBM TJ Watson Research Center. His research interests are at the intersection of human-computer interaction, computational social science, and applied AI. He’s a senior member of IEEE and has published research papers at conferences such as AAAI Conference on Artificial Intelligence and ACM Conference on Human Factors in Computing Systems. His work has been featured in outlets such as the Wall Street Journal and VentureBeat. He holds an MS from Carnegie Mellon University and a PhD from City University of Hong Kong.

Presentations

Handtrack.js: Building gesture-based interactions in the browser using TensorFlow.js Session

Victor Dibia explores the state of the art for machine learning in the browser using Tensorflow.js and dives into its use in the design of Handtrack.js—a library for prototyping real-time hand-tracking interactions in the browser

Tulsee Doshi is the product lead for Google’s ML fairness effort, where she leads the development of Google-wide resources and best practices for developing more inclusive and diverse products. Previously, Tulsee worked on the YouTube recommendations team. She earned her BS in symbolic systems and MS in computer science from Stanford University.

Presentations

Build more inclusive TensorFlow pipelines with fairness indicators Session

ML continues to drive monumental change across products and industries. But as we expand ML to even more sectors and users, it's ever more critical to ensure that these pipelines work well for all users. Tulsee Doshi and Christina Greer announce the launch of Fairness Indicators, built on top of TensorFlow Model Analysis, which allows you to measure and improve algorithmic bias.

Jared Duke is a software engineer on the Google Brain team leading performance efforts for TensorFlow Lite. Previously, he improved mobile VR for Daydream and mobile web browsing for Chrome at Google.

Presentations

TensorFlow Lite: ML for mobile and IoT devices Keynote

TensorFlow Lite makes it really easy to execute machine learning on mobile phones and microcontrollers. Jared Duke and Sarah Sirajuddin explore on-device ML and the latest updates to TensorFlow Lite model conversion, optimization, hardware acceleration, and ready-to-use model gallery. They also showcase demos and production use cases for TensorFlow Lite on phones and microcontrollers.

Yann Dupis is a machine learning engineer and privacy researcher at Dropout Labs. Previously, he was an actuary at the largest insurance company in Canada in reinsurance and then in research and development, and he managed a data science team at Deloitte in San Francisco, working with several Fortune 500 enterprises in the consumer and product industry. He holds a MASc in electrical and computer engineering from Institut Superieur d’Electronique de Paris. In his free time, you can find him surfing at Ocean Beach or indoor rock climbing in San Francisco.

Presentations

Privacy-preserving machine learning with TensorFlow and TF Encrypted Tutorial

People take advantage of machine learning across many facets of life. However, use cases dealing with highly sensitive data have been slow to adopt machine learning. Jason Mancuso and Yann Dupis take a deep dive into how to build and serve privacy-preserving machine learning models using TF Encrypted and the TensorFlow ecosystem.

Jordan Edwards is a principal program manager at Microsoft, working on machine learning frameworks and tools. He focuses on all aspects of ML ops, bringing machine learning workflows to production by augmenting existing DevOps (CI/CD) practices to account for the complexity of model training, validation, deployment, and monitoring.

Presentations

Hands-on deep learning with TensorFlow 2.0 and Azure 2-Day Training

Maxim Lukiyanov, Aashish Bhateja, Jordan Edwards, and Mehrnoosh Samekihow explore how AzureML helps data scientists be more productive when working through developing TensorFlow models for production. You'll see the whole model development lifecycle from training through deployment, ML ops, and all the way to model interpretability.

Kemal El Moujahid is the product director for TensorFlow at Google. He’s passionate about solving big problems with AI and building vibrant developer communities. Previously, Kemal led M, Facebook’s virtual assistant, the Messenger Platform, and Wit.ai. Kemal graduated from the Ecole Polytechnique and Telecom Paris, and holds an MBA from the Stanford Graduate School of Business.

Presentations

TensorFlow community announcements Keynote

Kemal El Moujahid divulges exciting developments for the TensorFlow community. Join in to learn how the TensorFlow team provides new and improved resources for developers and enterprises to succeed.

Úlfar Erlingsson is a research scientist on the Brain team at Google, working primarily on privacy and security of deep learning systems. Previously, Úlfar led computer security research at Google and was a researcher at Microsoft Research and associate professor at Reykjavik University. Úlfar was cofounder and CTO of the Internet security startup GreenBorder Technologies and director of privacy protection at deCODE genetics. Úlfar holds a PhD in computer science from Cornell University.

Presentations

TensorFlow Privacy: Learning with differential privacy for training data Session

When evaluating ML models, it can be difficult to tell the difference between what the models have generalized from the training and what the models have simply memorized. And that difference can be crucial in some ML tasks, such as when ML models are trained using sensitive data. Ulfar Erlingsson explains how to offer strong privacy guarantees for ML training data by using TensorFlow Privacy.

Pengfei Fan is a senior heterogeneous computing engineer at Alibaba Cloud. Previously, he worked on GPU compute architecture at NVIDIA. Pengfei is focused on designing and implementing virtualization and scheduling systems for heterogeneous infrastructure to accelerate AI applications and improve hardware use.

Presentations

HARP: An efficient and elastic GPU-sharing system Session

Pengfei Fan and Lingling Jin explain an efficient and elastic GPU-sharing system for users who do research and development with TensorFlow.

Will Fletcher is a machine learning (ML) researcher at Datatonic, where he concentrates on the technological progress of the company. He contributes an understanding of the most advanced methods in ML, along with experience in research and an eye for innovation. Previously, his academic career began as a chemist at Oxford; later, he moved to UCL for a further MSc in computational statistics and ML. Project and research work aside, Will delivers training days for companies to help them get started with ML. He believes in continuous education and learning as an essential part of technical excellence. This passion extends into his personal life, where he plays with math, programming and puzzles.

Presentations

Effective sampling methods within TensorFlow input functions Session

Many real-world machine learning applications require generative or reductive sampling of data. Laxmi Prajapat and William Fletcher demonstrate sampling techniques applied to training and testing data directly inside the input function using the tf.data API.

Marina Rose Geldard (Mars) is a technologist from Down Under in Tasmania. Entering the world of technology relatively late as a mature-age student, she has found her place in the world: an industry where she can apply her lifelong love of mathematics and optimization. She compulsively volunteers at industry events, dabbles in research, and serves on the executive committee for her state’s branch of the Australian Computer Society (ACS) as well as the AUC. She’s writing Practical Artificial Intelligence with Swift for O’Reilly and working on machine learning projects to improve public safety through public CCTV cameras in her hometown of Hobart.

Presentations

Swift for TensorFlow in 3 hours Tutorial

Mars Geldard, Tim Nugent, and Paris Buttfield-Addison say you're wrong if you think Swift is just for app developers. Swift for TensorFlow provides the power of TensorFlow with all the advantages of Python (and complete access to Python libraries) and Swift—the safe, fast, incredibly capable open source programming language; Swift for TensorFlow is the perfect way to learn deep learning and Swift.

Aurélien Géron is a machine learning consultant at Kiwisoft and author of the best-selling O’Reilly book Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow. Previously, he led YouTube’s video classification team, was a founder and CTO of Wifirst, and was a consultant in a variety of domains: finance (JPMorgan and Société Générale), defense (Canada’s DOD), and healthcare (blood transfusion). He also published a few technical books (on C++, WiFi, and internet architectures), and he’s a lecturer at the Dauphine University in Paris. He lives in Singapore with his wife and three children.

Presentations

Natural language processing using transformer architectures Session

Transformer architectures have taken the field of natural language processing (NLP) by storm and pushed recurrent neural networks to the sidelines. Aurélien Géron examines transformers and the amazing language models based on them (e.g., BERT and GPT 2) and shows how you can use them in your projects.

Production ML pipelines with TensorFlow Extended (TFX) 2-Day Training

Aurélien Géron dives into creating production ML pipelines with TensorFlow Extended (TFX) and using TFX to move from ML coding to ML engineering. You'll walk through the basics and put your first pipeline together, then learn how to customize TFX components and perform deep analysis of model performance.

Josh Gordon is a developer advocate at Google AI and teaches applied deep learning at Columbia University and machine learning at Pace University. He has over a decade of machine learning experience to share. You can find him on Twitter as @random_forests.

Presentations

Introduction to TensorFlow 2.0: Easier for beginners and more powerful for experts Session

TensorFlow 2.0 is all about ease of use, and there has never been a better time to get started. Joshua Gordon walks you through three styles of model-building APIs, complete with code examples.

Martin Gorner is a developer advocate at Google, where he focuses on parallel processing and machine learning. Martin is passionate about science, technology, coding, algorithms, and everything in between. He spent his first engineering years in the Computer Architecture Group of ST Microlectronics, then spent the next 11 years shaping the nascent ebook market at Mobipocket, which later became the software part of the Amazon Kindle and its mobile variants. He’s the author of the successful TensorFlow Without a PhD series. He graduated from Mines Paris Tech.

Presentations

Fast and lean data science with TPUs Session

Neural networks are now shipping in consumer-facing projects. Enterprises need to train and ship them fast, and data scientists want to waste less time on endless training. Martin Gorner explains how Google Tensor Processing Units (TPUs) are here to help.

Recurrent neural networks without a PhD Tutorial

Many problems deemed "impossible" only five years ago have now been solved by deep learning—from playing Go to recognizing what’s in an image to translating languages. Martin Gorner leads a hands-on introduction to recurrent neural networks and TensorFlow. Join in to discover what makes RNNs so powerful for time series analysis.

Christina Greer is a software engineer on the Google Brain team. She focuses specifically on machine learning fairness in the context of model evaluation and understanding, and scaling up solutions for ML fairness to support many teams across Google. Previously, Christina worked on building infrastructure to support diverse Google products: Google Assistant, Cloud Dataflow, and ads. Working in this area of ML fairness allows her to combine building infrastructure at Google scale with advancing efforts to avoid creating or reinforcing existing biases. Christina earned her BS in computer science from the University of Kansas.

Presentations

Build more inclusive TensorFlow pipelines with fairness indicators Session

ML continues to drive monumental change across products and industries. But as we expand ML to even more sectors and users, it's ever more critical to ensure that these pipelines work well for all users. Tulsee Doshi and Christina Greer announce the launch of Fairness Indicators, built on top of TensorFlow Model Analysis, which allows you to measure and improve algorithmic bias.

Priya Gupta is a software engineer on the TensorFlow team at Google, where she works on making it easier to run TensorFlow in a distributed environment. She’s passionate about technology and education and wants machine learning to be accessible to everyone. Previously, she worked at Coursera and on the mobile ads team at Google.

Presentations

Scaling TensorFlow using tf.distribute Session

Join Taylor Robie and Priya Gupta to learn how you can use tf.distribute to scale your machine learning model on a variety of hardware platforms ranging from commercial cloud platforms to dedicated hardware. You'll learn tools and tips to get the best scaling for your training in TensorFlow.

Sandeep Gupta is a product manager at Google, where he helps develop and drive the road map for TensorFlow—Google’s open source library and framework for machine learning—for supporting machine learning applications and research. His focus is on improving TensorFlow’s usability and driving adoption in the community and enterprise. Sandeep is excited about how machine learning and AI are transforming lives in a variety of ways, and he works with the Google team and external partners to help create powerful, scalable solutions for all. Previously, Sandeep was the technology leader for advanced imaging and analytics research and development at GE Global Research with specific emphasis on medical imaging and healthcare analytics.

Presentations

Introduction to machine learning in JavaScript using TensorFlow.js Tutorial

Join Sandeep Gupta and Brijesh Krishnaswami in this hands-on tutorial to learn to build and deploy machine learning (ML) models using JavaScript with official documentation, examples, and code labs from the TensorFlow team.

TensorFlow.js: Bringing machine learning to JavaScript Keynote

JavaScript is the most widely used programming language in the world, and with TensorFlow.js, you can bring the power of TensorFlow and machine learning to your JavaScript application. Sandeep Gupta and Joseph Paul Cohen introduce the TensorFlow.js library and display the amazing possibilities of combining machine learning with JavaScript-based web, mobile, and server-side applications.

Kevin Haas is a senior engineering manager on the research team at Google, driving the open source adoption of TensorFlow Extended, one of Google’s production ML platforms. Previously, Kevin was an engineering leader for multiple machine learning and infrastructure efforts in Google cloud, research, and infrastructure teams, and he led knowledge and search infrastructure efforts in multiple internet and software companies, including IBM, Microsoft, and Yahoo. He earned an MS from Stanford University in computer science in dual specializations of distributed systems and databases.

Presentations

TFX: Production ML pipelines with TensorFlow Session

ML development often focuses on metrics, delaying work on deployment and scaling issues. So Kevin Haas takes a deep dive into TensorFlow Extended.

Adam Hammond is a solution architect at Quantiphi, a deep learning and artificial intelligence solutions company, where he’s actively involved in developing and delivering solutions in the healthcare and insurance industries (both of which often call for interpretable models). Adam holds an MBA from Bentley University and an undergraduate degree in economics.

Presentations

Tagging cancer recurrence through machine learning Session

Asif Hasan and Adam Hammond dive into how TensorFlow and the Cloud Machine Learning Engine (CMLE) helped a healthcare provider develop a solution designed to predict the patient encounters associated with recurrence of cancer.

Asif Hasan is the cofounder of Quantiphi, a category-defining applied AI and big data software and services provider. He has over 15 years of experience in technology services, healthcare, and financial services industries working on a variety of initiatives such as building applied AI and advanced analytics capabilities at a global scale, postmerger integration, supply-chain operations, business transformation, and professional services. 

Previously, Asif led a global team of analytics and data science professionals focused on developing leading-edge analytical algorithms and solutions for business decision support for a multi-billion-dollar global healthcare services business including customer experience, service delivery, supply chain, and professional services. He holds an MBA from McCallum Graduate school of Business at Bentley University and participated in executive education programs at Harvard Business School.

Presentations

Tagging cancer recurrence through machine learning Session

Asif Hasan and Adam Hammond dive into how TensorFlow and the Cloud Machine Learning Engine (CMLE) helped a healthcare provider develop a solution designed to predict the patient encounters associated with recurrence of cancer.

Shengsheng (Shane) Huang is a software architect at Intel and an Apache Spark committer and PMC member, leading the development of large-scale analytical applications and infrastructure on Spark in Intel. Her area of focus is big data and distributed machine learning, especially deep (convolutional) neural networks. Previously at the National University of Singapore (NUS), her research interests are large-scale vision data analysis and statistical machine learning.

Shengsheng(Shane)Huang是英特尔的软件架构师,也是Apache Spark的贡献者和PMC成员。她领导着英特尔基于Spark的大规模分析应用和基础架构的开发。她关注的领域是大数据和分布式机器学习,尤其是深度(卷积)神经网络。她之前就读于新加坡国立大学(NUS),研究兴趣是大规模视觉数据分析和统计机器学习。

Presentations

Building AI to play the FIFA video game using distributed TensorFlow Session

Shengsheng Huang details her experience and insights about building AI to play the FIFA video game using distributed TensorFlow

Hamel Husain is a data scientist at GitHub, who is focused on creating the next generation of developer tools powered by machine learning. His work involves extensive use of natural language and deep learning techniques to extract features from code and text. Previously, Hamel was a data scientist at Airbnb where he worked on growth marketing and at DataRobot where he helped build automated machine learning tools for data scientists. Hamel can be reached on Twitter

Presentations

Automating your developer workflow on GitHub with TensorFlow Session

Software development is central to machine learning, regardless of if you're prototyping in a Jupyter notebook or building a service for millions of users. Hamel Husain, Omoju Miller, Michal Jastrzebski, and Jeremy Lewi explore how to use a freely available, natural language dataset to build practical applications for anyone who writes software using TensorFlow.

Ankit Jain is a senior research scientist at Uber AI Labs, the machine learning research arm of Uber. His work primarily involves the application of deep learning methods to a variety of Uber’s problems ranging from forecasting and food delivery to self-driving cars. Previously, he worked in variety of data science roles at Bank of America, Facebook, and other startups. He co-authored a book on machine learning titled TensorFlow Machine Learning Projects. Additionally, he’s been a featured speaker in many of the top AI conferences and universities across the US, including UC Berkeley, O’Reilly AI Conference, etc. He earned his MS from UC Berkeley and BS from IIT Bombay (India).

Presentations

Enhance recommendations in Uber Eats with graph convolutional networks Session

Ankit Jain and Piero Molino detail how to generate better restaurant and dish recommendations in Uber Eats by learning entity embeddings using graph convolutional networks implemented in TensorFlow.

Michał Jastrzębski is staff data engineer at GitHub, where he builds machine learning infrastructure for internal use. Previously, he was an architect at Intel’s Open Source Technology Center. Michał has a long experience in cloud technologies like OpenStack and Kubernetes, both as an operator and contributor. As former leader of OpenStack Kolla, he managed a community of more than 200 people and almost 40 companies. Michal has been involved with machine learning on Kubernetes communities like Kubeflow.

Presentations

Automating your developer workflow on GitHub with TensorFlow Session

Software development is central to machine learning, regardless of if you're prototyping in a Jupyter notebook or building a service for millions of users. Hamel Husain, Omoju Miller, Michal Jastrzebski, and Jeremy Lewi explore how to use a freely available, natural language dataset to build practical applications for anyone who writes software using TensorFlow.

Lingling Jin is a senior manager at Alibaba, where she focuses on heterogeneous infrastructures to accelerate AI applications and improve hardware use. Previously, she was part of NVIDIA’s Compute Architecture Group. She earned her PhD at the University of California, Riverside.

Presentations

HARP: An efficient and elastic GPU-sharing system Session

Pengfei Fan and Lingling Jin explain an efficient and elastic GPU-sharing system for users who do research and development with TensorFlow.

Megan Kacholia is a vice president of engineering within Google’s Research organization. Her team’s work spans machine learning in research as well as production, including products such as TensorFlow. Her passion is building effective teams and addressing barriers to help Googlers do their best work. Previously, Megan had a long tenure in Google’s Ads organization, where she ran the serving system for Google’s DisplayAds business.

Presentations

The latest from TensorFlow Keynote

Megan Kacholia outlines the latest TensorFlow product announcements and updates. You'll learn more about how Google's latest innovations provide a comprehensive ecosystem of tools for developers, enterprises, and researchers who want to push state-of-the-art machine learning and build scalable ML-powered applications.

Al Kari is CEO and principal consultant at Manceps, where he leads the company’s mission to augment human capabilities with machine intelligence, with a focus on blending machine learning and artificial intelligence with cloud computing and big data technologies. Al is a Google Developer Expert (GDE) in machine learning, organizer of the TensorFlow-Northwest and OpenStack Northwest user groups, and a strong advocate for open source AI and cloud technologies. Previously, Al was a global cloud evangelist at Microsoft, where he helped top-tier ISV partners onboard on the Microsoft Azure Platform. Al started his career in the mid-’90s as a software architect by founding Softwarehouse overseas before moving to the United States. He later held product and services leadership roles at Dell, where he helped build the company’s virtualization and cloud computing services portfolio; cofounded DetaCloud, a boutique OpenStack engineering powerhouse; and was a principal cloud architect at Red Hat, where he was responsible for helping customers build enterprise-ready cloud infrastructure. A frequent speaker at major industry conventions, Al has been an outspoken advocate for building the future of open artificial intelligence and cloud technologies in support of academic, industrial, and scientific development. He is a standing member of the Cloud Advisory Council, the Linux Professional Institute, and the OpenStack Foundation.

Presentations

Don’t beat the market, beat the bots: Adversarial networks in finance Session

Automated investing has brought an immense amount of stability to the market, but it has also brought predictability. Garrett Lander and Al Kari explore if an adversarial network can game the behavior of automated investors by learning the patterns in market activity to which they are most vulnerable.

Konstantinos (Gus) Katsiapis is the über tech lead of TensorFlow Extended (TFX), an end-to-end machine learning platform based on TensorFlow. He’s worked on Sibyl, a massive scale machine learning system (precursor to TensorFlow) widely used at Google and he was an avid user of machine learning infrastructure while leading mobile display ads quality machine learning team at Google. Previously, Gus gathered knowledge and experience at Amazon, Calian, the Ontario Ministry of Finance, Independent Electricity System Operator, and Computron. He earned a master’s degree in computer science with a specialization in artificial intelligence from Stanford University, and a bachelor’s degree in mathematics, majoring in computer science and minoring in economics from the University of Waterloo.

Presentations

TFX: An end-to-end ML platform for everyone Keynote

Konstantinos Katsiapis dives into TensorFlow Extended (TFX), which has evolved as the ML platform solution within Alphabet over the past decade.

Meenakshi Kaushik is a product manager for Cisco Container Platform, an enterprise-grade Kubernetes offering that supports GPU and Kubeflow for hybrid AI and ML workloads. Meenakshi is interested in the AI and ML space and is excited to see how the technology can enhance human well-being and productivity.

Presentations

Hyperparameter tuning for TensorFlow using Katib and Kubeflow Tutorial

Neelima Mukiri and Meenakshi Kaushik demonstrate how to automate hyperparameter tuning for a given dataset using Katib and Kubeflow. Katib can be easily run on a laptop or in a distributed production deployment, and Katib jobs and configuration can be easily ported to any Kubernetes cluster.

Nicolas Kowalski is a senior software engineer at Criteo. His work focuses on developing the platforms and tools that are used by all of Criteo to create any kind of machine learning model, train them, serve them online, and monitor their behavior. Previously, Nicolas earned a PhD in applied mathematics from Paris University Pierre and Marie Curie and spent some time in academia, where he published eight papers in international journals and conferences, including the best paper award at the 2012 International Meshing Roundtable.

Presentations

How Criteo optimized and sped up its TensorFlow models by 10x and served them under 5 ms Session

Criteo real-time bidding of ad spaces requires its TensorFlow (TF) models to make online predictions in less than 5 ms. Nicolas Kowalski and Axel Antoniotti explain why Criteo moved away from high-level APIs and rewrote its models from scratch, reimplementing cross-features and hashing functions using low-level TF operations, in order to factorize as much as possible all TF nodes in its model.

Brijesh Krishnaswami is a technical program manager on the TensorFlow team at Google. He has a master’s degree in computer science and two decades of experience in software development at various technology companies. You can find him on LinkedIn.

Presentations

Introduction to machine learning in JavaScript using TensorFlow.js Tutorial

Join Sandeep Gupta and Brijesh Krishnaswami in this hands-on tutorial to learn to build and deploy machine learning (ML) models using JavaScript with official documentation, examples, and code labs from the TensorFlow team.

Unlocking the power of machine learning for your JavaScript applications with TensorFlow Session

Kangyi Zhang, Brijesh Krishnaswami, and Joseph Paul Cohen take a deep dive into the TensorFlow.js ecosystem: how to bring an existing machine learning model into your JavaScript (JS) app, retrain the model with your data, and go beyond the browser to other JS platforms with live demos of models and featured apps (WeChat virtual plugin from L’Oréal and a radiology diagnostic tool from Mila).

Valliappa Lakshmanan is tech lead at Google Cloud focusing on data and machine learning. He’s the author of Data Science on GCP (O’Reilly), coauthor of BigQuery: The Definitive Guide (O’Reilly), and multiple Coursera courses.

Presentations

Machine learning with SQL and TensorFlow on Google Cloud Platform 2-Day Training

Valliappa Lakshmanan introduces you to using Google Cloud Platform to design and build machine learning (ML) models and how to deploy them into production. You'll walk through the process of building a complete machine learning pipeline from ingest and exploration to training, evaluation, deployment, and prediction.

Garrett Lander is a machine learning architect at Manceps, an ML consulting agency based out of Portland, Oregon. Garrett works with clients ranging from those taking their first steps into automation to seasoned ML practitioners looking to optimize their production models. Garrett is especially interested in the growing areas of AI pen-tests and ethicality, as well as the effort to build models that improve on human decision making without inheriting its shortcomings.

Presentations

Don’t beat the market, beat the bots: Adversarial networks in finance Session

Automated investing has brought an immense amount of stability to the market, but it has also brought predictability. Garrett Lander and Al Kari explore if an adversarial network can game the behavior of automated investors by learning the patterns in market activity to which they are most vulnerable.

Vitaly Lavrukhin is a senior applied research scientist at NVIDIA, working on deep learning algorithms for speech and language technologies. Previously, he conducted research to solve computer vision problems with deep learning methods at Samsung R&D Institute Russia.

Presentations

Speech Recognition with OpenSeq2Seq Session

OpenSeq2Seq provides a large set of state-of-the-art models and building blocks for automatic speech recognition (Jasper, wav2letter, DeepSpeech2), speech synthesis (Centaur, Tacotron2), and natural language processing. Jason Li and Vitaly Lavrukhin explore large vocabulary speech recognition and speech command recognition tasks to solve these problems with OpenSeq2Seq.

Joohoon Lee is a principal product manager for AI inference software at NVIDIA. Previously, he led the automotive deep learning software solutions team focusing on the production deployment of neural networks in DRIVE AGX platform using TensorRT. His expertise includes quantization, sparsity optimization, compilers, GPU, and AI accelerator architecture design. Joohoon received his BS and MS in electrical and computer engineering from Carnegie Mellon University.

Presentations

Faster inference in TensorFlow 2.0 with TensorRT Session

TensorFlow 2.0 offers high performance for deep learning inference through a simple API. Siddharth Sharma and Joohoon Lee take a deep dive into how to optimize an app using TensorRT with the new Keras APIs in TensorFlow 2.0. You'll learn tips and tricks to get the highest performance possible on GPUs and see examples of debugging and profiling tools by NVIDIA and TensorFlow.

Jeremy Lewi is a cofounder and lead engineer for the Kubeflow project at Google—an effort to help developers and enterprises deploy and use ML cloud natively everywhere. He’s been building on Kubernetes since its inception, starting with Dataflow and then moving onto Cloud ML Engine and now Kubeflow.

Presentations

Automating your developer workflow on GitHub with TensorFlow Session

Software development is central to machine learning, regardless of if you're prototyping in a Jupyter notebook or building a service for millions of users. Hamel Husain, Omoju Miller, Michal Jastrzebski, and Jeremy Lewi explore how to use a freely available, natural language dataset to build practical applications for anyone who writes software using TensorFlow.

Jason (Jing Yao) Li is a deep learning software engineer on the AI applications team at NVIDIA. He earned his BASc and MScAC at the University of Toronto working with Roger Grosse and Jimmy Ba. His research focus is on sequence-to-sequence models and speech, specifically in the domains of speech synthesis and speech recognition.

Presentations

Speech Recognition with OpenSeq2Seq Session

OpenSeq2Seq provides a large set of state-of-the-art models and building blocks for automatic speech recognition (Jasper, wav2letter, DeepSpeech2), speech synthesis (Centaur, Tacotron2), and natural language processing. Jason Li and Vitaly Lavrukhin explore large vocabulary speech recognition and speech command recognition tasks to solve these problems with OpenSeq2Seq.

Tianhui Michael Li is the founder and president of the Data Incubator, a data science training and placement firm. Michael bootstrapped the company and navigated it to a successful sale to the Pragmatic Institute. Previously, he headed monetization data science at Foursquare and has worked at Google, Andreessen Horowitz, J.P. Morgan, and D.E. Shaw. He’s a regular contributor to the Wall Street JournalTech CrunchWiredFast CompanyHarvard Business ReviewMIT Sloan Management ReviewEntrepreneurVenture Beat, Tech Target, and O’Reilly. Michael was a postdoc at Cornell Tech, a PhD at Princeton, and a Marshall Scholar in Cambridge.

Presentations

How to embed AI in your business 2-Day Training

Rich Ott and Michael Li lead a nontechnical overview of AI and data science. You’ll learn common techniques, how to apply them in your organization, and common pitfalls to avoid. You'll pick up the language and develop a framework to be able to effectively engage with technical experts and use their input and analysis for your business’s strategic priorities and decision making.

Tommy Li is a software developer at IBM focusing on cloud, container, and infrastructure technology. He’s worked on various developer journeys that provide use cases on cloud-computing solutions, such as Kubernetes, microservices, and hybrid cloud deployments. He’s passionate about machine learning and big data.

Presentations

Running TFX end to end in hybrid clouds leveraging Kubeflow Pipelines Session

TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines. It provides a configuration framework and shared libraries to integrate common components needed to define, launch, and monitor your machine learning system. Animesh Singh, Pete MacKinnon, and Tommy Li demonstrate how to run TFX in hybrid cloud environments.

Mike Liang is a senior product manager for TensorFlow at Google Research. He has over a decade of experience in machine learning and digital advertising from leading Google’s Asia Pacific display ads product strategy to building big data startups in China. Mike holds a PhD from Stanford University and a BS from the University of California San Diego.

Presentations

TensorFlow Hub: The platform to share and discover pretrained models for TensorFlow Keynote

Machine learning is a difficult skill to master for the many developers who are starting to use TensorFlow. Many developers use TensorFlow today, yet the majority of software developers out there have yet to learn machine learning. Mike Liang dives into TensorFlow Hub, designed to help developers make better and faster user of machine learning in their products.

Chao Liu is a software developer at AMD, where he works on the open source high-performance deep learning library miOpen. His interests include the development of parallel algorithms and numerical methods for a variety of applications, including deep learning and physics based simulation. Previously, he developed techniques for computational fluid dynamics, finite element analysis, iterative solvers, and mesh generations on shared and distributed-memory machines.

Presentations

Modular convolution considered beneficial Session

Jack Chung, Chao Liu, and Daniel Lowell explore breaking convolution algorithms into modular pieces to be better fused with graph compilers such as accelerated linear algebra (XLA).

Zac Yung-Chun Liu is a machine learning researcher at Stanford University, where he specializes in machine learning, artificial intelligence applications, remote sensing imagery processing, and geospatial analysis. His deep learning work includes computer vision, image classification, segmentation, object detection, and natural language processing related to disease ecology and shark conservation.

Presentations

Building deep learning applications using TensorFlow to combat schistosomiasis Session

Schistosomiasis is a debilitating parasitic disease that affects more than 250 million people worldwide. Zac Yung-Chun Liu, Andy Chamberlin, Susanne Sokolow, Giulio De Leo, and Ton Ngo detail how to build and deploy deep learning applications to detect disease transmission hotspots, make interventions more efficient and scalable, and help governments and stakeholders make data-driven decisions.

Ben Lorica is the chief data scientist at O’Reilly Media. Ben has applied business intelligence, data mining, machine learning, and statistical analysis in a variety of settings, including direct marketing, consumer and market research, targeted advertising, text mining, and financial engineering. His background includes stints with an investment management company, internet startups, and financial services.

Presentations

Thursday keynote welcome Keynote

TensorFlow World program chairs Ben Lorica and Edd Wilder-James welcome you to the second day of keynotes.

Thursday opening welcome Keynote

Program Chairs, Ben Lorica and Edd Wilder-James open the second day of keynotes.

Wednesday keynote welcome Keynote

TensorFlow World program chairs Ben Lorica and Edd Wilder-James welcome you to the first day of keynotes.

Wednesday opening welcome Keynote

Program Chairs, Edd Wilder-James and Ben Lorica open the first day of keynotes.

Daniel Lowell is the team lead and software architect for miOpen, AMD’s deep learning GPU kernels library. Previously, he worked at AMD Research in the high-performance computing (HPC) arena, in compiler technology and reliability. His interests include deep learning, brain-machine interfaces, autocode generation, and HPC.

Presentations

Modular convolution considered beneficial Session

Jack Chung, Chao Liu, and Daniel Lowell explore breaking convolution algorithms into modular pieces to be better fused with graph compilers such as accelerated linear algebra (XLA).

Nathan Luehr is a senior developer technology engineer at NVIDIA, where he works to accelerate deep learning frameworks. His background is in theoretical chemistry. He holds a doctoral degree from Stanford University, where he worked to accelerate electronic structure calculations on GPUs.

Presentations

Accelerating training, inference, and ML applications on NVIDIA GPUs Tutorial

Maggie Zhang, Nathan Luehr, and Josh Romero give you a sneak peak of software components from NVIDIA’s software stack so you can get the best out of your end-to-end AI applications on modern NVIDIA GPUs. They also examine features and tips and tricks to optimize your workloads right from data loading, processing, training, inference, and deployment.

Maxim Lukiyanov is a principle program manager on the Azure Machine Learning team at Microsoft. He works on large scale deep learning training.

Presentations

Hands-on deep learning with TensorFlow 2.0 and Azure 2-Day Training

Maxim Lukiyanov, Aashish Bhateja, Jordan Edwards, and Mehrnoosh Samekihow explore how AzureML helps data scientists be more productive when working through developing TensorFlow models for production. You'll see the whole model development lifecycle from training through deployment, ML ops, and all the way to model interpretability.

Pete MacKinnon is a principal software engineer in the AI Center of Excellence at Red Hat. He’s actively involved in the open source Kubeflow project to bring TensorFlow machine learning workloads to container environments (Kubernetes and OpenShift).

Presentations

Running TFX end to end in hybrid clouds leveraging Kubeflow Pipelines Session

TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines. It provides a configuration framework and shared libraries to integrate common components needed to define, launch, and monitor your machine learning system. Animesh Singh, Pete MacKinnon, and Tommy Li demonstrate how to run TFX in hybrid cloud environments.

Jason Mancuso is a research scientist at Dropout Labs, the founder of Cleveland AI, and an active member of the AI Village at DEF CON and the OpenMined community. He works on novel methods of making machine learning more performant for privacy-preserving techniques, most notably by contributing to the TF Encrypted project. He’s worked on a variety of safety and security problems, including safe reinforcement learning, secure and verifiable agent auditing, and neural network robustness. His work with the Cleveland Clinic established a state-of-the-art blood test classification and demonstrated that machine learning can virtually eliminate the problem of medical malpractice due to contaminated blood samples.

Presentations

Privacy-preserving machine learning with TensorFlow and TF Encrypted Tutorial

People take advantage of machine learning across many facets of life. However, use cases dealing with highly sensitive data have been slow to adopt machine learning. Jason Mancuso and Yann Dupis take a deep dive into how to build and serve privacy-preserving machine learning models using TF Encrypted and the TensorFlow ecosystem.

Briac Marcatté is a staff machine learning engineer at Twitter.

Presentations

Reliable, high-scale TensorFlow inference pipelines at Twitter Session

Twitter heavily relies on Scala and the Java Virtual Machine (JVM) and contains a lot of expertise knowledge. Shajan Dasan and Briac Marcatté walk you through the problems Twitter has had to overcome to make its offering reliable and to provide a reliable TensorFlow inference to Twitter customer teams, and they explain Twitter's key insights.

Clemens Mewald is the director of product management, machine learning and data science at Databricks, where he leads the product team. Previously, he spent four years on the Google Brain team building ML infrastructure for Google, Google Cloud, and open source users, including TensorFlow and TensorFlow Extended (TFX). Clemens holds an MSc in computer science from UAS Wiener Neustadt, Austria, and an MBA from MIT Sloan.

Presentations

Managing the full TensorFlow training, tracking, and deployment lifecycle with MLflow (sponsored by Databricks) Session

MLflow is an open source platform to manage the machine learning lifecycle, including experiment tracking, reproducible runs, and model packaging. Clemens Mewald dives into new features that integrate MLflow with the TensorFlow ecosystem, like autologging of metrics and models from TensorFlow runs.

Omoju Miller is a machine learning engineer with GitHub. Previously, she co-led the nonprofit investment in computer science education for Google and served as a volunteer advisor to the Obama administration’s White House Presidential Innovation Fellows. She’s a member of the World Economic Forum Expert Network in AI.

Presentations

Automating your developer workflow on GitHub with TensorFlow Session

Software development is central to machine learning, regardless of if you're prototyping in a Jupyter notebook or building a service for millions of users. Hamel Husain, Omoju Miller, Michal Jastrzebski, and Jeremy Lewi explore how to use a freely available, natural language dataset to build practical applications for anyone who writes software using TensorFlow.

Piero Molino is a cofounder and senior research scientist at Uber AI Labs, where he works on natural language understanding and conversational AI. He’s the author of the open source platform Ludwig, a code-free deep learning toolbox.

Presentations

Enhance recommendations in Uber Eats with graph convolutional networks Session

Ankit Jain and Piero Molino detail how to generate better restaurant and dish recommendations in Uber Eats by learning entity embeddings using graph convolutional networks implemented in TensorFlow.

Laurence Moroney is a developer advocate on the Google Brain team at Google, working on TensorFlow and machine learning. He’s the author of dozens of programming books, including several best sellers, and a regular speaker on the Google circuit. When not Googling, he’s also a published novelist, comic book writer, and screenwriter.

Presentations

Zero to ML hero with TensorFlow 2.0 Tutorial

Get a programmer's perspective with Laurence Moroney from the basics of machine learning all the way up to building complex computer vision scenarios using convolutional neural networks and natural language processing with recurrent neural networks.

Neelima Mukiri is a principal engineer in the Cloud Platform Solutions Group at Cisco, working on the architecture and development of Cisco’s Container Platform. Previously, she worked on the core virtualization layer at VMware and systems software in Samsung Electronics.

Presentations

Hyperparameter tuning for TensorFlow using Katib and Kubeflow Tutorial

Neelima Mukiri and Meenakshi Kaushik demonstrate how to automate hyperparameter tuning for a given dataset using Katib and Kubeflow. Katib can be easily run on a laptop or in a distributed production deployment, and Katib jobs and configuration can be easily ported to any Kubernetes cluster.

Robby Neale is a senior software engineer at Google. He leads the tf.text effort on the NLX infrastructure team, focusing on expanding the capabilities of the TensorFlow platform to make creation of text-based models easier for developers.

Presentations

Building models with tf.text Session

There are many resources for building models from numeric data, which means processing text had to occur outside the model. Robby Neale walks you through Ragged Tensors and tf.text.

Ton Ngo is a senior software developer in the IBM Cognitive OpenTech Group at the IBM Silicon Valley Lab. Previously, he was with the IBM Research Lab at Yorktown and Almaden. He’s been active in the open source community for four years and is working on TensorFlow and deep learning. He was a core contributor in OpenStack for Magnum and Heat-Translator, focusing on the networking and storage support for container orchestrator such as Kubernetes. Ton frequently gives talks and programming tutorials on TensorFlow in San Francisco, Seattle, and New York and at OpenStack Summits worldwide. He has published papers on a wide range of subjects.

Presentations

Building deep learning applications using TensorFlow to combat schistosomiasis Session

Schistosomiasis is a debilitating parasitic disease that affects more than 250 million people worldwide. Zac Yung-Chun Liu, Andy Chamberlin, Susanne Sokolow, Giulio De Leo, and Ton Ngo detail how to build and deploy deep learning applications to detect disease transmission hotspots, make interventions more efficient and scalable, and help governments and stakeholders make data-driven decisions.

Fabio Nonato de Paula is a director of data science at Shell’s New Energies business. He’s an AI leader passionate about deploying AI into production through rapid software development and creative thinking. His latest interests include working at the interface of DevOps and data science to provide SaaS solutions based on Bayesian methods for machine learning, system engineering, and operations research applied to big data. His teams are responsible for delivering process and network models, large-scale ML models, and predictive analytics; and employing probabilistic graph models, causal inference, and Bayesian deep learning into the CI/CD pipeline. He fostered a culture to support and advocate for the deployment of large-scale GPU ML pipelines on cloud infrastructure.

Presentations

Scaling industrial AI model building using TensorFlow Probability and Kubeflow Pipelines Session

Fabio Nonato de Paula and Arun Karthi Subramaniyan showcase the development and deployment of large-scale system-of-systems probabilistic models, with evolutionary architecture search, using TensorFlow Probability and Kubeflow Pipelines for predicting complex events and phenomena, applied to anomaly detection and predictive maintenance in large scale industrial systems.

Dave Norman is the director of machine learning frameworks at Graphcore, where he heads the frameworks team and is the creator of the intelligence procession unit and Poplar Software. He’s been in software engineering for over 25 years, specializing in real-time, high-performance and embedded systems. Previously, he was at Hewlett Packard, writing control software for experimental wireless and broadband modems; has worked for various companies on drivers for novel hardware, 3G/4G base stations, and the tools chain for an FPGA-like architecture; and worked abroad in New Zealand developing real-time weather graphics.

Presentations

Targeting high-performance ML accelerators using XLA Session

Victoria Rege and David Norman dive into the software of optimization for new accelerators using TensorFlow and accelerated linear algebra (XLA).

Tim Nugent pretends to be a mobile app developer, game designer, tools builder, researcher, and tech author. When he isn’t busy avoiding being found out as a fraud, Tim spends most of his time designing and creating little apps and games he won’t let anyone see. He also spent a disproportionately long time writing his tiny little bio, most of which was taken up trying to stick a witty sci-fi reference in…before he simply gave up. He’s writing Practical Artificial Intelligence with Swift for O’Reilly and building a game for a power transmission company about a naughty quoll (a quoll is an Australian animal).

Presentations

Swift for TensorFlow in 3 hours Tutorial

Mars Geldard, Tim Nugent, and Paris Buttfield-Addison say you're wrong if you think Swift is just for app developers. Swift for TensorFlow provides the power of TensorFlow with all the advantages of Python (and complete access to Python libraries) and Swift—the safe, fast, incredibly capable open source programming language; Swift for TensorFlow is the perfect way to learn deep learning and Swift.

Babusi Nyoni is a Zimbabwean innovator focused on the uses of artificial intelligence on the African continent. In 2016, he created what Forbes magazine described as, “the world’s first AI football commentator” for the UEFA Champions League final. In the same year, he created a prototype for the prediction of human displacement in Africa using AI, and thereafter worked with UNHCR Innovation to actualize a pilot project in the same field. He founded the Ulwazi Accelerator in 2018 to equip young Zimbabweans with the skills needed to contribute to the global digital economy. In 2019, he created an app for the early diagnosis of Parkinson’s disease and presented his findings at Oxford University on the Skoll World Forum stage. Babusi has a strong passion for fresh new ideas that will change the lives of those around him and is a firm believer that AI is shaping the technological zeitgeist worldwide.

Presentations

From dance to diagnosis: How Tensorflow.js is shaping AI in Africa Session

In 2018 Triple Black created a dance app that used Tensorflow.js-powered pose estimation on mobile phones to rate a popular South African dance known as "iVosho." Babusi Nyoni unpacks the possibilities for AI in disadvantaged African communities and explains how and why the company turned this dance app into a tool to diagnose Parkinson's disease.

Shin-ichiro Okamoto is the Vice President of Data Science Division at Actapio, Inc. f.k.a. YJ America, Inc. Shin-ichiro is working at Actapio on behalf of Yahoo Japan Corporation. He is developing AutoML with TensorFlow Extended, and is also leading the AI research and development of Yahoo Japan Corporation.

Presentations

Introduction of Hilbert AutoML with TensorFlow Extended (TFX) at Yahoo! JAPAN Session

Hilbert is an AI framework with TensorFlow Extended (TFX) at Yahoo! JAPAN, which provides AutoML to create production level deep learning models automatically. Hilbert is currently used by over 20 services of Yahoo! JAPAN. In this session, we will show in detail how to achieve production level AutoML and introduce service use cases at Yahoo! JAPAN.

Krzys Ostrowski is a research scientist at Google AI, focusing on developing programming abstractions for machine learning in large-scale distributed environments. He earned his PhD in computer science from Cornell University in the area of distributed systems and programming languages.

Presentations

A journey into the world of federated learning with TensorFlow Federated Session

Krzysztof Ostrowski dives into federated learning (FL)—an approach to machine learning where a shared model is trained across many clients that keep their training data local—and goes hands-on with FL using TensorFlow Federated (TFF). He demonstrates step-by-step how to train your TensorFlow model in a federated environment, implement custom federated computations, and set up large simulations.

Richard Ott is a data scientist in residence at the Data Incubator, where he combines his interest in data with his love of teaching. Previously, he was a data scientist and software engineer at Verizon. Rich holds a PhD in particle physics from the Massachusetts Institute of Technology, which he followed with postdoctoral research at the University of California, Davis.

Presentations

How to embed AI in your business 2-Day Training

Rich Ott and Michael Li lead a nontechnical overview of AI and data science. You’ll learn common techniques, how to apply them in your organization, and common pitfalls to avoid. You'll pick up the language and develop a framework to be able to effectively engage with technical experts and use their input and analysis for your business’s strategic priorities and decision making.

Presentations

Getting involved in the TensorFlow community Session

Joana Carrasqueira and Nicole Pang explain how you can be a part of the growing TensorFlow ecosystem and become a contributor through code, documentation, education, and community leadership.

Sean Park is a senior malware scientist in the Machine Learning Group at Trend Micro, as part of an elite team of researchers solving highly difficult problems in the battle against cybercrime. His main research focus is deep learning-based threat detection, including generative adversarial malware clustering, metamorphic malware detection using semantic hashing and Fourier transform, malicious URL detection with attention mechanism, macOS X malware outbreak detection, semantic malicious script autoencoder, and heterogeneous neural networks for Android APK detection. Previously, he worked for Kaspersky, FireEye, Symantec, and Sophos. He also created a critical security system for banking malware at a top Australian bank.

Presentations

Generative malware outbreak detection Session

Practical defense systems require precise detection during the malware outbreaks where only a handful of samples are available. Sean Park demonstrates how to detect in-the-wild malware samples with a single training sample of a kind with the help of TensorFlow's flexible architecture in implementing a novel variable length generative adversarial autoencoder.

Aalok Patwa (he/him) is a sophomore at Archbishop Mitty High School, California, interested in machine learning and healthcare. He’s done several research projects in the past that have won awards at the regional, state, and national level. He’s also committed to outreach, imparting his knowledge about computer science and medicine to the broader public. Aalok is the president of the computer programming club at his high school and an avid participant in speech and debate. He won the first place category award in the Synopsys science fair in 2018 and 2019, was a national finalist at the Broadcom MASTERS Science Fair in 2016, earned a Raytheon achievement award at the California State science fair in 2016, and was a speech and debate national qualifier.

Presentations

TensorFlow and medicine: Using deep learning for real-time segmentation of colon polyps Session

The public health sector is growing rapidly, and with new methods of data collection comes a need for new analyzation methods. Aalok Patwa explains how to use TensorFlow to create a deep learning model that detects, localizes, and segments colon polyps from colonoscopy image and video. You'll gain technical knowledge of TensorFlow, Keras, and ideas for the application of TensorFlow in medicine.

Laxmi Prajapat is a senior data scientist at Datatonic, with involvement in end-to-end project delivery, including stakeholder management, data exploration, machine learning, algorithm design, automation, and productionization solutions on Google Cloud. After a masters in astrophysics from UCL, Laxmi has held several technical roles in industry. She’s at her happiest when learning new things and challenging herself. Laxmi is always looking to expand her knowledge and apply it practically, especially in the fields of machine learning and engineering. Outside of work, she enjoys exploring new cuisines or finding a book to get lost in.

Presentations

Effective sampling methods within TensorFlow input functions Session

Many real-world machine learning applications require generative or reductive sampling of data. Laxmi Prajapat and William Fletcher demonstrate sampling techniques applied to training and testing data directly inside the input function using the tf.data API.

Shashank Prasanna is a senior AI and machine learning evangelist at Amazon Web Services, where he focuses on helping engineers, developers, and data scientists solve challenging problems with machine learning. Previously, he worked at NVIDIA, MathWorks (makers of MATLAB), and Oracle in product marketing and software development roles focused on machine learning products. Shashank holds an MS in electrical engineering from Arizona State University.

Presentations

TensorFlow on AWS 2-Day Training

Amazon Web Services (AWS) offers a breadth and depth of services to easily build, train, and deploy TensorFlow models. Shashank Prasanna gives you hands-on experience working with these services.

Victoria Rege is the head of strategic partnerships at Graphcore, where she works with key customers and leads research and universities AI engagements. She has over a decade of experience in the semiconductor space. Previously, she held several leadership positions at NVIDIA from global alliances, product marketing, and campaigns to the founding of the GPU Technology Conference; and she has worked in the hedge fund space as executive director for the Hedge Fund Business Operations Association. Victoria is a frequent contributor to ACM SIGGRAPH and is AR, MR & VR Chair for the SIGGRAPH 2019 Conference. She’s also an active member of the Consumer Technology Association’s AI Working Group.

Presentations

Targeting high-performance ML accelerators using XLA Session

Victoria Rege and David Norman dive into the software of optimization for new accelerators using TensorFlow and accelerated linear algebra (XLA).

Taylor Robie is software engineer at Google, where he’s a member of the TensorFlow high-level APIs team focusing on performance with a particular emphasis on out-of-the-box performance of Keras. Previously, he was a maintainer of the TensorFlow official models repository and optimized several of the Google MLPerf submissions.

Presentations

Scaling TensorFlow using tf.distribute Session

Join Taylor Robie and Priya Gupta to learn how you can use tf.distribute to scale your machine learning model on a variety of hardware platforms ranging from commercial cloud platforms to dedicated hardware. You'll learn tools and tips to get the best scaling for your training in TensorFlow.

Josh Romero is a developer technology engineer at NVIDIA. He has extensive experience in GPU computing from porting and optimizing high-performance computing (HPC) applications to more recent work with deep learning. Josh earned his PhD from Stanford University, where his research focused on developing new computational fluid dynamics methods to better exploit GPU hardware.

Presentations

Accelerating training, inference, and ML applications on NVIDIA GPUs Tutorial

Maggie Zhang, Nathan Luehr, and Josh Romero give you a sneak peak of software components from NVIDIA’s software stack so you can get the best out of your end-to-end AI applications on modern NVIDIA GPUs. They also examine features and tips and tricks to optimize your workloads right from data loading, processing, training, inference, and deployment.

Brennan Saeta is a software engineer on the Google Brain team leading the Swift for TensorFlow project. Previously, he was the TensorFlow tech lead for Cloud TPUs.

Presentations

Swift for TensorFlow Session

Paige Bailey and Brennan Saeta walk you through Swift for TensorFlow, a next-generation machine learning platform that leverages innovations like first-class differentiable programming to seamlessly integrate deep neural networks with traditional AI algorithms and general purpose software development.

Mehrnoosh Sameki is a technical program manager at Microsoft responsible for leading the product efforts on machine learning interpretability within the Azure Machine Learning platform. Previously, she was a data scientist at Rue Gilt Groupe, incorporating data science and machine learning in retail space to drive revenue and enhance personalized shopping experiences of customers. She earned her PhD degree in computer science at Boston University.

Presentations

Hands-on deep learning with TensorFlow 2.0 and Azure 2-Day Training

Maxim Lukiyanov, Aashish Bhateja, Jordan Edwards, and Mehrnoosh Samekihow explore how AzureML helps data scientists be more productive when working through developing TensorFlow models for production. You'll see the whole model development lifecycle from training through deployment, ML ops, and all the way to model interpretability.

Kaz Sato is a staff developer advocate on the cloud platform team at Google, where he leads the developer advocacy team for machine learning and data analytics products such as TensorFlow, the Vision API, and BigQuery. Kaz has been leading and supporting developer communities for Google Cloud for over seven years. He’s a frequent speaker at conferences, including Google I/O 2016, Hadoop Summit 2016 San Jose, Strata and Hadoop World 2016, and Google Next 2015 NYC and Tel Aviv, and he has hosted FPGA meetups since 2013.

Presentations

AutoML Vision and Edge TPU: Bringing TensorFlow Lite models to edge devices Session

Kaz Sato walks you through AutoML Vision, which allows you to upload labeled images, press a "train" button, and wait for a day to get an image recognition model with state-of-the-art accuracy. Without any ML expertise, you can easily train the model in the cloud, export the TensorFlow Lite model, and use it on mobile devices, Rasberry Pi, and Edge TPU with super low latency and power consumption.

Robert Schroll is a data scientist in residence at the Data Incubator. Previously, he held postdocs in Amherst, Massachusetts, and Santiago, Chile, where he realized that his favorite parts of his job were teaching and analyzing data. He made the switch to data science and has been at the Data Incubator since. Robert holds a PhD in physics from the University of Chicago.

Presentations

Introduction to TensorFlow 2-Day Training

The TensorFlow library provides for the use of computational graphs with automatic parallelization across resources, ideal architecture for implementing neural networks. Robert Schroll introduces TensorFlow's capabilities in Python, moving from building machine learning algorithms piece by piece to using the Keras API provided by TensorFlow with several hands-on applications.

Andrew Selle is a senior staff software engineer for TensorFlow Lite at Google and is one of its initial architects. He’s also worked on improvements to the core and API of TensorFlow. Previously, he worked extensively in research and development of highly parallel numerical physical simulation techniques for physical phenomena for film and physically based rendering. He worked on several Walt Disney Animation Films including Frozen and Zootopia. He holds a PhD in computer science from Stanford University.

Presentations

TensorFlow Lite: Beginner to expert Tutorial

Andrew Selle introduces you to TensorFlow Lite and takes you through the conversion, performance, and optimization path while using Android and iOS applications.

Sudipta Sengupta is a senior principal technologist and director at AWS, where he leads new initiatives in artificial intelligence and deep learning. Previously, he headed an end-to-end innovation agenda at Microsoft Research, spanning cloud networking, storage, and data management; was at Bell Labs working on internet routing, optical switching, network security, wireless networks, and network coding. He has shipped his research in many industry-leading, award-winning products and services. Sudipta is ACM Fellow and IEEE Fellow. He was awarded the IEEE William R. Bennett Prize and the IEEE Leonard G. Abraham Prize for his work on computer networking. Sudipta earned a PhD and an MS in EECS from Massachusetts Institute of Technology (MIT), and a BTech in computer science and engg from Indian Institute of Technology (IIT), Kanpur, India. He was awarded the President of India Gold Medal at IIT-Kanpur for graduating at the top of his class across all disciplines.

Presentations

Integrating deep learning accelerators with TensorFlow Session

Sudipta Sengupta dives into his experience with Amazon Elastic Inference and AWS Inferentia with TensorFlow in the AWS cloud.

Siddharth Sharma is a senior technical marketing manager for accelerated computing at NVIDIA. Previously, Siddharth was a product marketing manager for Simulink and Stateflow at MathWorks, working closely with automotive and aerospace companies to adopt model-based designs for creating control software.

Presentations

Faster inference in TensorFlow 2.0 with TensorRT Session

TensorFlow 2.0 offers high performance for deep learning inference through a simple API. Siddharth Sharma and Joohoon Lee take a deep dive into how to optimize an app using TensorRT with the new Keras APIs in TensorFlow 2.0. You'll learn tips and tricks to get the highest performance possible on GPUs and see examples of debugging and profiling tools by NVIDIA and TensorFlow.

Animesh Singh is a senior technical staff manager and program director at IBM, leading IBM AI OSS strategy working with the IBM Watson and Cloud Platform. He leads machine learning and deep learning initiatives and works with communities and customers to design and implement deep learning, machine learning, and cloud computing frameworks. He has a proven track record of driving design and implementation of private and public cloud solutions from concept to production. In his decade-plus at IBM, Animesh has worked on cutting-edge projects for IBM enterprise customers in the telco, banking, and healthcare industries, particularly focusing on cloud and virtualization technologies, and led the design and development of the first IBM public cloud offering.

Presentations

Running TFX end to end in hybrid clouds leveraging Kubeflow Pipelines Session

TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines. It provides a configuration framework and shared libraries to integrate common components needed to define, launch, and monitor your machine learning system. Animesh Singh, Pete MacKinnon, and Tommy Li demonstrate how to run TFX in hybrid cloud environments.

Sarah Sirajuddin is an engineering director working on TensorFlow at Google. She leads the teams working on on-device machine learning, TensorFlow Extended, and efforts around training models for the best accuracy and performance with Google’s cutting edge infrastructure, including TensorFlow and Tensor Processing Units (TPUs).

Presentations

TensorFlow Lite: ML for mobile and IoT devices Keynote

TensorFlow Lite makes it really easy to execute machine learning on mobile phones and microcontrollers. Jared Duke and Sarah Sirajuddin explore on-device ML and the latest updates to TensorFlow Lite model conversion, optimization, hardware acceleration, and ready-to-use model gallery. They also showcase demos and production use cases for TensorFlow Lite on phones and microcontrollers.

Daniel Situnayake leads developer advocacy for TensorFlow Lite at Google. Previously, he cofounded Tiny Farms, the first US company using automation to produce insect protein at industrial scale, and he began his career lecturing in automatic identification and data capture at Birmingham City University.

Presentations

TensorFlow Lite: Solution for running ML on-device Session

Pete Warden and Daniel Situnayake take you through TensorFlow Lite, TensorFlow’s lightweight cross-platform solution for mobile and embedded devices, which enables on-device machine learning inference with low latency, high performance, and a small binary size.

Susanne Sokolow is a senior research associate at Stanford University and UC Canta Barbara. She’s also the executive director of the newly founded Center for Disease Ecology, Health, and Development at Stanford University and is a cofounder and an executive board member of the Upstream Alliance, an initiative joining partners across the globe in research for schistosomiasis reduction. She studies basic and applied research at the interface of disease ecology, health, and development. Her research program seeks natural solutions to modern health and environmental problems plaguing the developing world.

Presentations

Building deep learning applications using TensorFlow to combat schistosomiasis Session

Schistosomiasis is a debilitating parasitic disease that affects more than 250 million people worldwide. Zac Yung-Chun Liu, Andy Chamberlin, Susanne Sokolow, Giulio De Leo, and Ton Ngo detail how to build and deploy deep learning applications to detect disease transmission hotspots, make interventions more efficient and scalable, and help governments and stakeholders make data-driven decisions.

Zak Stone is the product manager for Cloud TPUs on the Google Brain team and the founder of the TensorFlow Research Cloud (TFRC) at Google. He’s interested in making hardware acceleration for machine learning universally accessible and useful. Previously, Zak founded a mobile-focused deep learning startup that was acquired by Apple, and while at Apple, Zak contributed to the privacy-preserving on-device face identification technology in iOS 10 and macOS Sierra that was announced at the Apple Worldwide Developers Conference (WWDC) 2016. Zak holds a PhD in computer vision.

Presentations

Great TensorFlow Research Cloud projects from around the world (and how to start your own) Session

Join Zak Stone to see how researchers all over the world are expanding the frontiers of ML using free Cloud Tensor Processing Unit (TPU) capacity from the TensorFlow Research Cloud.

Arun Subramaniyan is the vice president of data science and analytics at Baker Hughes, where he leads the global data science and analytics team at Baker Hughes Digital. His team develops deep learning-augmented domain analytics for all aspects of the oil and gas industry. Previously, Arun was at the GE Global Research Center, where he led the development of the Digital Twin Framework, which enabled several thousand engineers to build advanced models efficiently. The asset-specific cumulative damage modeling techniques his team pioneered have saved millions of dollars for several businesses. As a Six Sigma Master Black Belt, he developed advanced techniques and tools for efficiently modeling large-scale systems like jet engines and accelerated design by 3x–4x. Arun is a prolific researcher with a PhD in aerospace engineering from Purdue University with over 50 international publications that have been cited more than 600 times. He’s a recipient of the Hull Award from GE, which honors technologists for their outstanding technical impact

Presentations

Scaling industrial AI model building using TensorFlow Probability and Kubeflow Pipelines Session

Fabio Nonato de Paula and Arun Karthi Subramaniyan showcase the development and deployment of large-scale system-of-systems probabilistic models, with evolutionary architecture search, using TensorFlow Probability and Kubeflow Pipelines for predicting complex events and phenomena, applied to anomaly detection and predictive maintenance in large scale industrial systems.

Theodore Summe is Head of Product for Cortex, Twitter’s central ML organization. His team of product managers work across Applied Research, ML services & ML platform, working with all Twitter product teams to apply and advance ML applications to meet Twitter’s customers’ needs.

Presentations

Accelerating ML @ Twitter Keynote

Twitter employs ML throughout its product to deliver value for its customers. Theodore will share a glimpse into ML @ Twitter and how Cortex is working to accelerate ML to better serve customer needs, partnering with Tensorflow.

Mikhail Szugalew is a machine learning developer at the Knowledge Society. A year ago, he knew nothing about machine learning, object detection, or the physical challenges the visually impaired face. With a strong will, he set out to learn about AI and make an impact in the world. Over the course of just eight months, he researched and developed a prototype device to assist the visually impaired with their navigational challenges. His endeavors show how machine learning technologies can impact the future. His experiences at just the age of 16 are a great example of how we live in a world where new powerful technologies can be leveraged by anyone, and even teenagers can make a difference.

Presentations

How machine learning can empower a 16-year-old to make crossing the street safer Session

When Mikhail Szugalew discovered that the visually impaired face huge navigational challenges with tasks as simple as crossing the street, he decided to do something about it at just the age of 16, using his experience with TensorFlow to develop object-detection models. He highlights his insights, struggles, process, takeaways, and vision for a better future.

KC Tung is an AI architect at Microsoft. Previously, he has been a cloud architect, ML engineer, data scientist with hands-on experience and success in the development and serving of AI, deep learning, computer vision, and natural language processing (NLP) models in many enterprise use case-driven architectures, using open source machine learning libraries such as TensorFlow, Keras, PyTorch, and H2O. His specialties are AI and ML in end-to-end model and data structure design, testing, and serving in the cloud or on-premises; and technical core, the design of experiments, hypothesis development, and reference architecture for AI and ML in cloud-centric implementation. KC holds a PhD in molecular biophysics from the University of Texas Southwestern Medical Center in Dallas, TX.

Presentations

A novel solution for a data augmentation and bias problem in NLP using TensorFlow Session

Join KC Tung to discover a way to use TensorFlow to solve a natural language processing (NLP) model bias problem with data augmentation for an enterprise customer (one of the largest airlines in the world). KC leverages hidden gems in tf.data and the new API to easily find a novel use for text generation and found it surprisingly improved his NLP model.

Pete Warden is the technical lead of the mobile and embedded TensorFlow Group on Google’s Brain team.

Presentations

TensorFlow Lite: Solution for running ML on-device Session

Pete Warden and Daniel Situnayake take you through TensorFlow Lite, TensorFlow’s lightweight cross-platform solution for mobile and embedded devices, which enables on-device machine learning inference with low latency, high performance, and a small binary size.

Edd Wilder-James is a strategist at Google, where he is helping build a strong and vital open source community around TensorFlow. A technology analyst, writer, and entrepreneur based in California, Edd previously helped transform businesses with data as vice president of strategy for Silicon Valley Data Science. Formerly Edd Dumbill, Edd was the founding program chair for the O’Reilly Strata conferences and chaired the Open Source Convention for six years. He was also the founding editor of the peer-reviewed journal Big Data. A startup veteran, Edd was the founder and creator of the Expectnation conference management system and a cofounder of the Pharmalicensing online intellectual property exchange. An advocate and contributor to open source software, Edd has contributed to various projects such as Debian and GNOME and created the DOAP vocabulary for describing software projects. Edd has written four books, including Learning Rails (O’Reilly).

Presentations

Thursday keynote welcome Keynote

TensorFlow World program chairs Ben Lorica and Edd Wilder-James welcome you to the second day of keynotes.

Thursday opening welcome Keynote

Program Chairs, Ben Lorica and Edd Wilder-James open the second day of keynotes.

Wednesday keynote welcome Keynote

TensorFlow World program chairs Ben Lorica and Edd Wilder-James welcome you to the first day of keynotes.

Wednesday opening welcome Keynote

Program Chairs, Edd Wilder-James and Ben Lorica open the first day of keynotes.

Craig Wiley is the director of product for Google Cloud’s AI Platform. Previously, Craig spent nine years at Amazon, most recently as the general manager of Amazon SageMaker, AWS’s machine learning platform, and he led pricing and analytics in Amazon’s third-party seller business. Craig has a deep belief in democratizing the power of data; he pushes to improve the tooling for experienced users while seeking to simplify it for the growing set of less-experienced users. Outside of work, he enjoys spending time with his family, eating delicious meals, and enthusiastically struggling through small home improvement projects.

Presentations

Enterprise-ready TensorFlow in the Cloud (sponsored by Google Cloud Platform) Keynote

Enterprise adoption of AI placed new expectations on TensorFlow. Craig Wiley explores how to maximize your TensorFlow performance and experience in the cloud. You’ll learn how to speed up your software development and ensure the longevity and reliability of your AI-powered enterprise applications.

Sam Witteveen is a developer expert for machine learning at Google. He has extensive experience in startups and mobile applications and helps developers and companies create smarter applications with machine learning. He’s especially passionate about deep learning and AI in the fields of natural language and conversational agents. Sam regularly shares his knowledge at events and trainings across Asia and is co-organizer of the Singapore TensorFlow and Deep Learning group.

Presentations

TensorFlow and TPUs in the real world: Converting deep learning projects to train faster Session

Sam Witteveen divulges tips and tricks to take advantage of Tensor Processing Units (TPUs) in TensorFlow 2.0 and to take a current deep learning project and convert it to something that runs smoothly and quickly on cloud TPUs.

Li Xu is a software engineer on the health machine learning team at Twitter, working on the development of machine learning technologies for health, security, and privacy. Previously, he was a software engineer on the security machine learning platform team at Uber, working on the architecture development of machine learning platform for security, and a researcher at Yahoo Labs, where he conducted state-of-the-art research on security, privacy, and machine learning. Li has shipped many inventions and technologies to Yahoo, Uber, and Twitter products. Nowadays, more than a billion users are using these products. His research interests lie in security and machine learning. He’s authored or coauthored papers in top-ranked journals, conferences, book chapter, and US patents. He served as a program committee member for top conferences of security, AI, and big data.

Presentations

Improving the health of public conversations on Twitter with TensorFlow Session

When people discuss on Twitter, Twitter wants to ensure that they can have respectful conversations with genuine people. Twitter relies on machine learning to improve the health of public conversations and information integrity. Li Xu and Yi Zhuang examine how Twitter uses TensorFlow to detect abusive, toxic, and spammy content, and promote healthy conversations on the platform.

Kangyi Zhang is a software engineer at Google Brain and a member of the TensorFlow.js team. He’s very excited about sharing how to do machine learning in the JavaScript world, concentrating on native TensorFlow execution under the Node.js runtime, and preparing data for machine learning model in JS. You can find him on GitHub @kangyizhang.

Presentations

Unlocking the power of machine learning for your JavaScript applications with TensorFlow Session

Kangyi Zhang, Brijesh Krishnaswami, and Joseph Paul Cohen take a deep dive into the TensorFlow.js ecosystem: how to bring an existing machine learning model into your JavaScript (JS) app, retrain the model with your data, and go beyond the browser to other JS platforms with live demos of models and featured apps (WeChat virtual plugin from L’Oréal and a radiology diagnostic tool from Mila).

Maggie Zhang is a solution architect at NVIDIA, where she works on deep learning frameworks. She earned her PhD in computer science and engineering from the University of New South Wales in Australia. Her research background includes GPU and CPU heterogeneous computing, compiler optimization, computer architecture, and deep learning.

Presentations

Accelerating training, inference, and ML applications on NVIDIA GPUs Tutorial

Maggie Zhang, Nathan Luehr, and Josh Romero give you a sneak peak of software components from NVIDIA’s software stack so you can get the best out of your end-to-end AI applications on modern NVIDIA GPUs. They also examine features and tips and tricks to optimize your workloads right from data loading, processing, training, inference, and deployment.

Yi Zhuang is a senior staff machine learning software engineer at Twitter, where he leads a team building a platform for working with ML models. He works on uniting ML practitioners around a single ML platform, bringing consistency to ML practices at Twitter. Previously, Yi led a team to develop a trillion-document-scale distributed search engine at Twitter. Yi holds an MS in computer science from Carnegie Mellon University. He loves cats and enjoys pondering over all things technical and logical.

Presentations

Improving the health of public conversations on Twitter with TensorFlow Session

When people discuss on Twitter, Twitter wants to ensure that they can have respectful conversations with genuine people. Twitter relies on machine learning to improve the health of public conversations and information integrity. Li Xu and Yi Zhuang examine how Twitter uses TensorFlow to detect abusive, toxic, and spammy content, and promote healthy conversations on the platform.

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