October 28–31, 2019
Schedule
Monday, October 28, 2019
8:00am
8:00am–9:00am Monday, October 28, 2019
Morning Coffee
(1h)
9:00am
Training
Aurélien Geron (Kiwisoft)
Average rating:
(5.00, 2 ratings)
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.
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Training
Valliappa Lakshmanan (Google)
Valliappa Lakshmanan shows you how to use 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.
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Training
Robert Schroll (The Data Incubator)
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.
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Training
Shashank Prasanna (Amazon Web Services),
vikrant kahlir (Amazon Web Services),
Rama Thamman (Amazon Web Services),
Shreyas Subramanian (Amazon)
Average rating:
(3.00, 2 ratings)
Amazon Web Services (AWS) offers a breadth and depth of services to easily build, train, and deploy TensorFlow models. Shashank Prasanna, Vikrant Kahlir, and Rama Thamman give you hands-on experience working with these services.
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Training
Maxim Lukiyanov (Microsoft),
Vaidyaraman Sambasivam (Microsoft),
Mehrnoosh Sameki (MERS) (Microsoft),
Santhosh Pillai (Microsoft)
Maxim Lukiyanov, Vaidyaraman Sambasivam, Mehrnoosh Samekihow, and Santhosh Pillai demonstrate 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 to deployment and ML ops to model interpretability.
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Edd Wilder-James (Google),
Martin Wicke (Google),
Omoju Miller (GitHub),
Edd Wilder-James (Google),
Joana Filipa Bernardo Carrasqueira (Google),
Chandni Shah (Google),
Jason Zaman (Light Labs),
Yifei Feng (Google),
Gunhan Gulsoy (Google Brain),
Edd Wilder-James (Google),
Sean Morgan (Two Six Labs),
Jason Zaman (Light Labs),
Karmel Allison (Google),
Martin Wicke (Google),
Margaret Maynard-Reid (Tiny Peppers)
Contributor Summit
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10:30am
10:30am–11:00am Monday, October 28, 2019
Morning Break
(30m)
12:30pm
12:30pm–1:30pm Monday, October 28, 2019
Lunch
(1h)
3:00pm
3:00pm–3:30pm Monday, October 28, 2019
Afternoon Break
(30m)
7:00pm
Event
Get to know your fellow attendees over dinner. We've made reservations for you at some of the most sought-after restaurants in town. This is a great chance to make new connections and sample some of the great cuisine Santa Clara has to offer.
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Tuesday, October 29, 2019
8:00am
8:00am–9:00am Tuesday, October 29, 2019
Morning Coffee
(1h)
9:00am
Sandeep Gupta (Google),
Brijesh Krishnaswami (Google)
Join Sandeep Gupta and Brijesh Krishnaswami to learn how to build and deploy machine learning models using JavaScript, with official documentation, examples, and code labs from the TensorFlow team.
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Robert Crowe (Google)
Average rating:
(4.00, 4 ratings)
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 outlines what's involved in creating a production ML pipeline and walks you through working code.
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Andrew Selle (Google)
Andrew Selle offers an introduction to TensorFlow Lite and takes you through the conversion, performance, and optimization path while using Android and iOS applications.
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9:00am–12:30pm Tuesday, October 29, 2019
TBC
Leonardo Apolonio (Clarabridge)
Average rating:
(2.00, 1 rating)
Leonardo Apolonio takes a deep dive into BERT and explains how you can use BERT to solve problems.
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Laurence Moroney (Google)
Average rating:
(3.50, 4 ratings)
Get a programmer's perspective on machine learning with Laurence Moroney, from the basics all the way up to building complex computer vision scenarios using convolutional neural networks and natural language processing with recurrent neural networks.
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Contributor Summit Day 2
If you already contribute to TensorFlow, or would like to, please join us for the inaugural TensorFlow Contributor Summit, colocated with TensorFlow World. We’ll be in Santa Clara, California, on October 28 and 29. Whether you work with code, docs, or the community, we’d love to have you there!
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10:30am
10:30am–11:00am Tuesday, October 29, 2019
Morning Break
(30m)
12:30pm
12:30pm–1:30pm Tuesday, October 29, 2019
Break
(1h)
1:30pm
Mars Geldard (University of Tasmania),
Tim Nugent (Lonely Coffee),
Paris Buttfield-Addison (Secret Lab)
Mars Geldard, Tim Nugent, and Paris Buttfield-Addison are here to prove Swift isn't 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.
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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.
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Maggie Zhang (NVIDIA),
Nathan Luehr (NVIDIA),
Josh Romero (NVIDIA),
Pooya Davoodi (NVIDIA),
Davide Onofrio (NVIDIA)
Average rating:
(4.00, 1 rating)
Maggie Zhang, Nathan Luehr, Josh Romero, Pooya Davoodi, and Davide Onofrio give you a sneak peek at 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.
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Average rating:
(4.00, 1 rating)
Jason Mancuso and Yann Dupis demonstrate how to build and deploy privacy-preserving machine learning models using TF Encrypted, PySyft-TensorFlow, and the TensorFlow ecosystem.
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Martin Gorner (Google)
Average rating:
(5.00, 3 ratings)
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.
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3:00pm
3:00pm–3:30pm Tuesday, October 29, 2019
Afternoon Break
(30m)
5:00pm
Event
Average rating:
(4.00, 2 ratings)
Join us for a fun, high-energy evening of five-minute talks—all aspiring to live up to the Ignite motto: Enlighten us, but make it quick.
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6:30pm
Event
Average rating:
(4.00, 2 ratings)
Join us at the TensorFlow World Poster Sessions to explore work from the cutting edge of TensorFlow and discuss it one-on-one with a great lineup of presenters.
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Wednesday, October 30, 2019
6:30am
Event
Join us for the TensorFlow World 5K Fun Run/Walk! You don’t have to be a serious runner. We encourage you to go at your own pace and stop to take in views of Santa Clara.
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7:30am
7:30am–9:00am Wednesday, October 30, 2019
Morning Coffee
(1h 30m)
8:00am
Event
Average rating:
(5.00, 1 rating)
Ready, set, network! Meet fellow attendees who are looking to connect at TensorFlow World. We'll gather before Wednesday and Thursday keynotes for an informal speed networking event. Be sure to bring your business cards—and remember to have fun.
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8:45am
Keynote
Average rating:
(5.00, 1 rating)
TensorFlow World program chairs Ben Lorica and Edd Wilder-James welcome you to the first day of keynotes.
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9:00am
9:25am
Keynote
Megan Kacholia (Google)
Average rating:
(4.00, 2 ratings)
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.
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9:40am
Keynote
Frederick Reiss (IBM)
Average rating:
(4.33, 3 ratings)
IBM has a long history of contributing to the open source projects that make the most difference to its clients, and the company has been working to build responsible solutions to enterprise data science problems for many years. Join Frederick Reiss to hear about IBM's role in open source software, TensorFlow, building AI solutions, and what IBM is excited about with this latest (2.0) release.
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9:45am
Keynote
Theodore Summe (Twitter)
Average rating:
(3.50, 2 ratings)
Twitter employs ML throughout its product to deliver value for its customers. Theodore Summe gives you a glimpse into ML at Twitter and explains how Cortex works to accelerate ML to better serve customer needs by partnering with TensorFlow.
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10:00am
Keynote
Craig Wiley (Google)
Average rating:
(4.00, 2 ratings)
Enterprise adoption of AI placed new expectations on TensorFlow. Craig Wiley details 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.
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10:10am
Keynote
Kemal El Moujahid (Google)
Average rating:
(4.00, 2 ratings)
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.
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10:30am
10:30am–11:00am Wednesday, October 30, 2019
Morning break sponsored by NVIDIA
(30m)
11:00am
Average rating:
(4.50, 2 ratings)
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, Michał Jastrzębski, and Jeremy Lewi show you how to use a freely available, natural language dataset to build practical applications for anyone who writes software using TensorFlow.
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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.
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Average rating:
(5.00, 2 ratings)
Criteo's 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.
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Clemens Mewald (Databricks)
Average rating:
(5.00, 1 rating)
Clemens Mewald offers an overview of the latest component of MLflow, a model registry that provides a collaborative hub where teams can share ML models, work together from experimentation to online testing and production, integrate with approval and governance workflows, and monitor ML deployments and their performance.
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Sam Witteveen (Red Dragon AI)
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.
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Joshua Gordon (Google)
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.
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11:50am
Zac Yung-Chun Liu (Stanford University),
Andy Chamberlin (Stanford University),
Susanne Sokolow (Stanford University | UC Santa Barbara),
Giulio De Leo (Stanford University),
Ton Ngo (IBM)
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.
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Hannes Hapke (SAP ConcurLabs)
Average rating:
(5.00, 2 ratings)
Hannes Hapke leads a deep dive into deploying TensorFlow models within minutes with TensorFlow Serving and optimizing your serving infrastructure for optimal throughput.
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Moderated by:
Deepak Bhadauria (Google)Panelists:
Saurabh Mishra (Quantiphi),
Upendra Sahu (Quantiphi),
Bhushan Jagyasi (Accenture),
David Beck (Cognizant),
Rahul Sarda (Wipro Limited)
Deepak Bhadauria, Saurabh Mishra, Upendra Sahu, Bhushan Jagyasi, David Beck, and Rahul Sarda share four real-world TensorFlow success stories from the banking, insurance, med tech, and nonprofit industries.
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Neil Truong, Kari Briski, and Khoa Ho walk you through their experience running TensorFlow at scale on GPU clusters like the DGX SuperPod and the Summit supercomputer. They explore the design of these large-scale GPU systems and detail how to run TensorFlow at scale using BERT and AI plus high-performance computing (HPC) applications as examples.
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TensorFlow 2.0 offers high performance for deep learning inference through a simple API. Siddharth Sharma and Joohoon Lee explain 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.
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Average rating:
(5.00, 2 ratings)
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.
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12:30pm
Event
Topic Table discussions are a great way to informally network with people in similar industries or interested in the same topics.
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Event
If you’re looking to make new professional connections and hear ideas for supporting inclusion, come to the diversity networking lunch.
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1:40pm
Average rating:
(4.00, 1 rating)
Josh Baer and Keshi Dai discuss how Spotify has historically used ML and explore how the introduction of TensorFlow and TFX in particular has standardized its ML workflows and improved its ability to bring ML-powered products to its users.
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Average rating:
(2.00, 1 rating)
Twitter heavily relies on Scala and the Java Virtual Machine (JVM) and contains a lot of expertise knowledge. Shajan Dasan and Briac Marcatté detail the problems Twitter has had to overcome to make its offering reliable and provide a reliable TensorFlow inference to Twitter customer teams.
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Kaz Sato (Google)
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.
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Animesh Singh (IBM)
Join Animesh Singh to learn how IBM leverages the power of open source to bring trust back in AI, using popular open source projects for adversarial AI defense and attacks, bias detection and mitigation, and datasets and model explainability.
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Sudipta Sengupta (AWS)
Average rating:
(2.00, 1 rating)
Sudipta Sengupta dives into his experience with Amazon Elastic Inference and AWS Inferentia with TensorFlow in the AWS cloud.
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Robby Neale (Google)
Average rating:
(2.00, 1 rating)
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.
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2:30pm
Pengfei Fan and Lingling Jin offer an overview of an efficient and elastic GPU-sharing system for users who do research and development with TensorFlow.
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Yong Tang (MobileIron)
In many applications where data is generated continuously, combining machine learning with streaming data is imperative to discover useful information in real time. Yong Tang explores TensorFlow I/O, which can be used to easily build a data pipeline with TensorFlow and stream frameworks such as Apache Kafka, AWS Kinesis, or Google Cloud PubSub.
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Krzysztof Ostrowski (Google)
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.
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Karthik Ramachandran (Google Cloud),
Kaz Sato (Google)
Karthik Ramachandran and Kaz Sato take a look at how you can use AI platform notebooks, deep learning virtual machines, and deep learning containers to build TensorFlow applications. You'll learn to maximize TensorFlow performance on Google Cloud by eliminating I/O bottlenecks and some tips and tricks for ensuring the longevity and reliability of your AI-powered enterprise applications.
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Martin Gorner (Google)
Average rating:
(4.00, 1 rating)
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's tensor processing units (TPUs) are here to help.
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Average rating:
(4.25, 4 ratings)
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.
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3:10pm
3:10pm–4:10pm Wednesday, October 30, 2019
Afternoon break sponsored by IBM
(1h)
4:10pm
Wisdom d'Almeida (Mila)
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. Wisdom provides a live demo of the model in action.
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SHIN-ICHIRO OKAMOTO (Actapio f.k.a. YJ America)
Hilbert is an AI framework that works 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. Shin-Ichiro Okamoto details how to achieve production-level AutoML and explores service use cases at Yahoo! JAPAN.
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Average rating:
(5.00, 2 ratings)
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.
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Average rating:
(2.00, 1 rating)
Victoria Rege and David Norman dive into the software optimization for new accelerators using TensorFlow and accelerated linear algebra (XLA).
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Average rating:
(5.00, 2 ratings)
Large-scale open source projects can be daunting, and one of the goals of TensorFlow is to be accessible to many contributors. Joana Carrasqueira and Nicole Pang share some great ways to get involved in TensorFlow, explain how its design and development works, and show you how to get started if you're new to machine learning or new to TensorFlow.
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5:00pm
Sean Park (Trend Micro)
Average rating:
(5.00, 1 rating)
Practical defense systems require precise detection during malware outbreaks with only a handful of available samples. 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.
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Juntai Zheng (Databricks)
Juntai Zheng explains how to use 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.
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Ulfar Erlingsson (Google Brain)
Average rating:
(5.00, 1 rating)
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. Úlfar Erlingsson explains how to offer strong privacy guarantees for ML training data by using TensorFlow Privacy.
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Manjunath Kudlur and Andy Hock describe the software that compiles TensorFlow to the recently announced Cerebras Wafer-Scale Engine (WSE) for deep learning.
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5:45pm
Event
Average rating:
(3.00, 1 rating)
Come enjoy delicious snacks and beverages with fellow attendees, speakers, and sponsors at the Expo Hall Reception, happening immediately after the afternoon sessions on Wednesday.
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7:00pm
Event
Average rating:
(5.00, 1 rating)
TensorFlow World attendees are invited to Great America for an unforgettable evening—filled with a haunted house, rides, games, and great food and drinks.
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Thursday, October 31, 2019
8:00am
Event
Ready, set, network! Meet fellow attendees who are looking to connect at TensorFlow World. We'll gather before Wednesday and Thursday keynotes for an informal speed networking event. Be sure to bring your business cards—and remember to have fun.
Read more.
8:00am–9:00am Thursday, October 31, 2019
Morning Coffee
(1h)
8:45am
Keynote
TensorFlow World program chairs Ben Lorica and Edd Wilder-James welcome you to the second day of keynotes.
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8:50am
Keynote
Average rating:
(3.00, 1 rating)
Konstantinos Katsiapis and Anusha Ramesh offer an overview of TensorFlow Extended (TFX), which has evolved as the ML platform solution within Alphabet over the past decade.
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9:05am
Keynote
Tony Jebara (Spotify)
Average rating:
(2.00, 1 rating)
Tony Jebara explains how Spotify improved user satisfaction with Home by building various components of the TFX ecosystem into its core ML infrastructure.
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9:15am
Keynote
Mike Liang (Google Research)
Average rating:
(3.00, 1 rating)
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 takes you through TensorFlow Hub, designed to help developers make better and faster user of machine learning in their products.
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9:25am
Keynote
Ujval Kapasi (NVIDIA)
Machine learning on NVIDIA GPUs and systems allows developers to solve problems that seemed impossible just a few years ago. Ujval Kapasi explains how software and hardware advances on GPUs impact development efforts across the community, both today and in the future.
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9:30am
Keynote
Anna Roth (Microsoft)
Average rating:
(4.50, 2 ratings)
It's never been easier to train machine learning models. With excellent open source tooling, lower compute techniques, and incredible educational material online, really anybody can start to train their own models today. Yet, Anna Roth explains, when domain experts try to transfer their expertise to an ML model, the results can be unpredictable.
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9:40am
Keynote
Average rating:
(5.00, 1 rating)
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, including model conversion, optimization, hardware acceleration, and a ready-to-use model gallery. They also showcase demos and production use cases for TensorFlow Lite on phones and microcontrollers.
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9:55am
Keynote
Ankur Narang (Hike)
Average rating:
(3.00, 1 rating)
Ankur Narang offers an overview of the cutting-edge AI-driven innovations on the Hike messaging platform, such as sticker recommendation with multilingual support—a key innovation driven by sophisticated natural language processing (NLP) algorithms.
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10:00am
Keynote
Average rating:
(4.00, 2 ratings)
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 showcase the amazing possibilities of combining machine learning with JavaScript-based web, mobile, and server-side applications.
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10:15am
Keynote
Average rating:
(3.00, 1 rating)
MLIR is TensorFlow's open source machine learning compiler infrastructure that addresses the complexity caused by growing software and hardware fragmentation and makes it easier to build AI applications. Chris Lattner and Tatiana Shpeisman explain how MLIR is solving this growing hardware and software divide and how it impacts you in the future.
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10:30am
10:30am–11:00am Thursday, October 31, 2019
Morning Break
(30m)
11:00am
Shengsheng Huang and Jason Dai detail their experience and insights about building AI to play the FIFA video game using distributed TensorFlow.
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Aurélien Geron (Kiwisoft)
Average rating:
(4.50, 6 ratings)
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.
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Va Barbosa and Paul Van Ec highlight the benefits of using TensorFlow.js and Node-RED together as an educational tool to engage developers and provide you with a powerful, creativity-inspiring platform for interacting and developing with machine learning models.
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Patricia Florissi (Dell EMC)
Patricia Florissi identifies some intrinsic patterns in the anatomy of emerging digital fabrics, including those demanding agility in adapting to change, in dynamically creating connectivity meshes, and in scaling in size and complexity to unprecedented rates.
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Mikhail Szugalew (The Knowledge Society)
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.
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Average rating:
(3.50, 2 ratings)
ML development often focuses on metrics, delaying work on deployment and scaling issues. So Robert Crowe takes a deep dive into TensorFlow Extended.
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11:50am
Average rating:
(1.00, 1 rating)
Automated investing has brought an immense amount of stability to the market, but it has also brought predictability. Garrett Lander and Al Kari examine if an adversarial network can game the behavior of automated investors by learning the patterns in market activity to which they are most vulnerable.
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KC Tung (Microsoft)
Average rating:
(4.00, 1 rating)
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 leveraged 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.
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Victor Dibia (Cloudera Fast Forward Labs)
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.
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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.
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Average rating:
(4.00, 2 ratings)
Pete Warden, Nupur Garg, and Matthew Dupuy 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.
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12:30pm
Event
Topic Table discussions are a great way to informally network with people in similar industries or interested in the same topics.
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1:40pm
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.
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When people discuss on Twitter, the company 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 promotes healthy conversations on the platform.
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Babusi Nyoni (Triple Black)
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.
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Alasdair Allan (Babilim Light Industries)
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.
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Raziel Alverez (Google)
Average rating:
(4.67, 3 ratings)
Raziel Alverez walks you through best current practices and future directions in core TensorFlow technology.
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2:30pm
Aalok Patwa (Archbishop Mitty High School)
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.
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Average rating:
(5.00, 1 rating)
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.
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Pengchong Jin (Google)
Pengchong Jin walks you through a typical development workflow on GCP for training and deploying an object detector to a self-driving car. He demonstrates how to train the state-of-the-art RetinaNet model fast using Cloud TPUs and scale up the model effectively on Cloud TPU pods. Pengchong also explains how to export a Tensor-RT optimized mode on GPU for inference.
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Margaret Maynard-Reid (Tiny Peppers)
Margaret Maynard-Reid walks you through end-to-end tf.Keras to TFLite to Android, with or without ML Kit.
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kangyi zhang (Google),
Brijesh Krishnaswami (Google),
Joseph Paul Cohen (Mila | University of Montreal),
Brendan Duke (ModiFace)
Kangyi Zhang, Brijesh Krishnaswami, Joseph Paul Cohen, and Brendan Duke 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).
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3:10pm
3:10pm–4:10pm Thursday, October 31, 2019
Afternoon Break
(1h)
4:10pm
Average rating:
(4.67, 3 ratings)
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.
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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).
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Keqiu Hu, Jonathan Hung, and Abin Shahab explore the challenges LinkedIn encountered and resolved to scale TensorFlow.
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Joe Bowser (Adobe)
Average rating:
(4.00, 1 rating)
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.
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Da-Cheng Juan (Google Research),
Sujith Ravi (Google AI)
Average rating:
(5.00, 1 rating)
Da-Cheng Juan and Sujith Ravi explain neural structured learning (NSL), an easy-to-use TensorFlow framework that both novice and advanced developers can use for training neural networks with structured signals.
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