Put AI to Work
April 15-18, 2019
New York, NY

Schedule: Implementing AI sessions

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9:00am - 5:00pm Monday, April 15 & Tuesday, April 16
Location: Gibson
Secondary topics:  Deep Learning and Machine Learning tools
SOLD OUT
Dylan Bargteil (The Data Incubator)
The TensorFlow library provides for the use of computational graphs, with automatic parallelization across resources. This architecture is ideal for implementing neural networks. Dylan Bargteil walks you through TensorFlow's capabilities in Python, teaching you how to build machine learning algorithms piece by piece and use the Keras API provided by TensorFlow with several hands-on applications. Read more.
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9:00am - 5:00pm Monday, April 15 & Tuesday, April 16
Location: Regent
Secondary topics:  Deep Learning and Machine Learning tools, Financial Services, Models and Methods, Temporal data and time-series
Francesca Lazzeri (Microsoft), Wee Hyong Tok (Microsoft), Krishna Anumalasetty (Microsoft)
Francesca Lazzeri, Wee Hyong Tok, and Krishna Anumalasetty walk you through the core steps for using Azure Machine Learning services to train your machine learning models both locally and on remote compute resources. Read more.
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9:00am - 5:00pm Monday, April 15 & Tuesday, April 16
Location: Clinton
Secondary topics:  Deep Learning and Machine Learning tools, Models and Methods, Text, Language, and Speech
SOLD OUT
Delip Rao (AI Foundation), Brian McMahan (Wells Fargo)
Average rating: ****.
(4.50, 2 ratings)
Delip Rao and Brian McMahan explore natural language processing with deep learning, walking you through neural network architectures and NLP tasks and teaching you how to apply these architectures for those tasks. Read more.
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9:00am12:30pm Tuesday, April 16, 2019
Location: Beekman
Secondary topics:  Deep Learning and Machine Learning tools
Mo Patel (Independent)
Average rating: *....
(1.50, 2 ratings)
Mo Patel leads a deep dive into all aspects of the PyTorch lifecycle via hands-on examples such as image classification, text classification, and linear modeling. Along the way, you'll explore other aspects of machine learning such as transfer learning, data modeling, and deploying to production with immersive labs. Read more.
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9:00am12:30pm Tuesday, April 16, 2019
Location: Petit Trianon
Secondary topics:  Deep Learning and Machine Learning tools, Ethics, Privacy, and Security
Rachel Bellamy (IBM Research), Kush Varshney (IBM Research), KARTHIKEYAN NATESAN RAMAMURTHY (IBM Research), Michael Hind (IBM Research AI)
Rachel Bellamy, Kush Varshney, Karthikeyan Natesan Ramamurthy, and Michael Hind explain how to use and contribute to AI Fairness 360—a comprehensive Python toolkit that provides metrics to check for unwanted bias in datasets and machine learning models and state-of-the-art algorithms to mitigate such bias. Read more.
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1:45pm5:15pm Tuesday, April 16, 2019
Location: Sutton South
Secondary topics:  Deep Learning and Machine Learning tools, Text, Language, and Speech
Average rating: *****
(5.00, 1 rating)
Justina Petraityte offers a hands-on walk-through of developing intelligent AI assistants based entirely on machine learning and using only the open source tools Rasa NLU and Rasa Core. You'll learn the fundamentals of conversational AI and best practices for developing AI assistants that scale and learn from real conversational data. Read more.
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1:45pm5:15pm Tuesday, April 16, 2019
Location: Trianon Ballroom
Secondary topics:  Models and Methods, Temporal data and time-series
Bruno Goncalves (Data For Science, Inc)
Average rating: ***..
(3.25, 4 ratings)
Time series are everywhere around us. Understanding them requires taking into account the sequence of values seen in previous steps and even long-term temporal correlations. Join Bruno Gonçalves to learn how to use recurrent neural networks to model and forecast time series and discover the advantages and disadvantages of recurrent neural networks with respect to more traditional approaches. Read more.
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11:05am11:45am Wednesday, April 17, 2019
Location: Rendezvous
Secondary topics:  Deep Learning and Machine Learning tools
joshua gordon (Google)
Average rating: *****
(5.00, 2 ratings)
Josh Gordon shares the very latest in TensorFlow, focusing on TensorFlow 2.0 and its easy-to-use eager execution. Josh also covers how to use TensorFlow's revised high-level API and details pitfalls and tricks to get better performance on accelerator hardware. Read more.
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11:05am11:45am Wednesday, April 17, 2019
Location: Trianon Ballroom
Secondary topics:  Deep Learning and Machine Learning tools, Edge computing and Hardware, Platforms and infrastructure
Mathew Salvaris (Microsoft), Fidan Boylu Uz (Microsoft)
Interested in deep learning models and how to deploy them on Kubernetes at production scale? Not sure if you need to use GPUs or CPUs? Mathew Salvaris and Fidan Boylu Uz help you out by providing a step-by-step guide to creating a pretrained deep learning model, packaging it in a Docker container, and deploying as a web service on a Kubernetes cluster. Read more.
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1:00pm1:40pm Wednesday, April 17, 2019
Location: Rendezvous
Secondary topics:  Deep Learning and Machine Learning tools
Catherine Ordun (Booz Allen Hamilton)
Average rating: ****.
(4.67, 3 ratings)
While building machine learning models for most large projects, data scientists typically design dozens of models using different combinations of hyperparameters, data configurations, and training settings. Catherine Ordun describes how to build your own machine learning model tracking leaderboard in Keras. Read more.
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1:00pm1:40pm Wednesday, April 17, 2019
Location: Trianon Ballroom
Secondary topics:  Edge computing and Hardware, Platforms and infrastructure, Reinforcement Learning, Retail and e-commerce, Temporal data and time-series
JIAN CHANG (Alibaba Group), Sanjian Chen (Alibaba Group)
Average rating: *****
(5.00, 5 ratings)
Jian Chang and Sanjian Chen outline the design of the AI engine built on Alibaba’s TSDB service, which enables fast and complex analytics of large-scale time series data in many business domains. Join in to see how TSDB empowers companies across various industries to better understand data trends, discover anomalies, manage risks, and boost efficiency. Read more.
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1:50pm2:30pm Wednesday, April 17, 2019
Location: Regent Parlor
Secondary topics:  Edge computing and Hardware
Simon Crosby (SWIM.AI)
Average rating: ****.
(4.00, 1 rating)
Today’s approach to processing streaming data is based on legacy big-data centric architectures, the cloud, and the assumption that organizations have access to data scientists to make sense of it all—leaving organizations increasingly overwhelmed. Simon Crosby shares a new architecture for edge intelligence that turns this thinking on its head. Read more.
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1:50pm2:30pm Wednesday, April 17, 2019
Location: Rendezvous
Secondary topics:  Platforms and infrastructure, Text, Language, and Speech
Jeremy Lewi (Google), Hamel Husain (GitHub)
Turning ML into magical products often requires complex distributed systems that bring with them a unique ML-specific set of infrastructure problems. Using AI to label GitHub issues as an example, Jeremy Lewi and Hamel Husain demonstrate how to use Kubeflow and Kubernetes to build and deploy ML products. Read more.
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1:50pm2:30pm Wednesday, April 17, 2019
Location: Trianon Ballroom
Secondary topics:  Media, Marketing, Advertising, Platforms and infrastructure
YU DONG (Facebook)
Average rating: ***..
(3.50, 2 ratings)
Yu Dong offers an overview of the why, what, and how of building a production-scale ML platform based on ongoing ML research trends and industry adoptions. Read more.
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2:40pm3:20pm Wednesday, April 17, 2019
Location: Rendezvous
Secondary topics:  AI in the Enterprise, Models and Methods, Text, Language, and Speech
Sumeet Vij (Booz Allen Hamilton), Matt Speck (Booz Allen Hamilton)
Sumeet Vij and Matt Speck showcase an innovative application of deep learning to power cognitive conversational agents. You'll learn how chatbots can overcome the limitations of limited training datasets by leveraging transfer learning and deep pretrained models for NLP and how machine learning can advance robotic process automation (RPA) from “robotic” to “cognitive” automation. Read more.
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2:40pm3:20pm Wednesday, April 17, 2019
Location: Trianon Ballroom
Secondary topics:  Deep Learning and Machine Learning tools, Media, Marketing, Advertising, Platforms and infrastructure
Yi Zhuang (Twitter), Nicholas Leonard (Twitter)
Average rating: *****
(5.00, 1 rating)
Twitter is a large company with many ML use cases. Historically, there have been many ways to productionize ML at Twitter. Yi Zhuang and Nicholas Leonard describe the setup and benefits of a unified ML platform for production and explain how the Twitter Cortex team brings together users of various ML tools. Read more.
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4:05pm4:45pm Wednesday, April 17, 2019
Location: Rendezvous
Secondary topics:  AI case studies, Deep Learning and Machine Learning tools, Reliability and Safety
Pradip Bose (IBM T. J. Watson Research Center)
Pradip Bose details a next-generation AI research project focused on creating "self-aware" AI systems that have built-in autonomic detection and mitigation facilities to avoid faulty or undesirable behavior in the field—in particular, cognitive bias and inaccurate decisions that are perceived as being unethical. Read more.
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4:55pm5:35pm Wednesday, April 17, 2019
Location: Rendezvous
Secondary topics:  Deep Learning and Machine Learning tools, Ethics, Privacy, and Security
Andrew Zaldivar (Google)
The development of AI is creating new opportunities to improve the lives of all people. It's also raising new questions about ways to build fairness, interpretability, and other moral and ethical values into these systems. Using Jupyter and TensorFlow, Andrew Zaldivar shares hands-on examples that highlight current work and recommended practices toward the responsible development of AI. Read more.
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4:55pm5:35pm Wednesday, April 17, 2019
Location: Trianon Ballroom
Secondary topics:  Computer Vision, Edge computing and Hardware, Platforms and infrastructure
Ted Way (Microsoft), Maharshi Patel (Microsoft), Aishani Bhalla (Microsoft)
Deep neural networks (DNNs) have enabled AI breakthroughs, but serving DNNs at scale has been challenging: Fast and cheap? Won’t be accurate. Fast and accurate? Won’t be cheap. Join Ted Way, Maharshi Patel, and Aishani Bhalla to learn how to use Python and TensorFlow to train and deploy computer vision models on Intel FPGAs with Azure Machine Learning and Project Brainwave. Read more.
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9:35am9:50am Thursday, April 18, 2019
Location: Grand Ballroom West
Secondary topics:  Platforms and infrastructure
Kim Hazelwood (Facebook)
Average rating: *****
(5.00, 1 rating)
Applied Machine Learning at Facebook Read more.
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11:05am11:45am Thursday, April 18, 2019
Location: Rendezvous
Secondary topics:  AI in the Enterprise, Automation in machine learning and AI, Platforms and infrastructure
Diego Oppenheimer (Algorithmia), Brendan Collins (Algorithmia)
Average rating: ****.
(4.00, 1 rating)
Diego Oppenheimer draws upon his work with thousands of developers across hundreds of organizations to discuss the tools and processes every business needs to automate model deployment and management so they can optimize model performance, control compute costs, maintain governance, and keep data scientists doing data science. Read more.
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1:00pm1:40pm Thursday, April 18, 2019
Location: Rendezvous
Secondary topics:  Deep Learning and Machine Learning tools, Platforms and infrastructure
Evan Sparks (Determined AI)
Average rating: *****
(5.00, 1 rating)
Building deep learning applications is hard. Building them repeatably is harder. Maintaining high computational performance during a repeatable deep learning development process is borderline impossible. Evan Sparks describes the key pitfalls associated with fast, repeatable model development and details what practitioners can do to avoid them and maintain a supercharged AI development workflow. Read more.
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2:40pm3:20pm Thursday, April 18, 2019
Location: Rendezvous
Secondary topics:  Deep Learning and Machine Learning tools
Magnus Hyttsten (Google)
Average rating: *****
(5.00, 1 rating)
Magnus Hyttsten explains how to use TensorFlow effectively in a distributed manner using best practices. Magnus covers using TensorFlow's new DistributionStrategies to get easy high-performance training with Keras models (and custom models) on multi-GPU setups as well as multinode training on clusters with accelerators. Read more.
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2:40pm3:20pm Thursday, April 18, 2019
Location: Trianon Ballroom
Secondary topics:  Computer Vision, Data and Data Networks, Ethics, Privacy, and Security, Reliability and Safety
Ryan Mukherjee (JHU/APL), Neil Fendley (JHU/APL)
Average rating: *****
(5.00, 1 rating)
While deep learning has led to many advancements in computer vision, most research has focused on ground-based imagery. Ryan Mukherjee and Neil Fendley offer an overview of functional Map of the World (fMoW), an ImageNet for satellite imagery built to address this issue, and explain how you can attack or defend these deep learning models. Read more.
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4:05pm4:45pm Thursday, April 18, 2019
Location: Regent Parlor
Secondary topics:  AI case studies, Financial Services, Temporal data and time-series
Aric Whitewood (WilmotML)
Average rating: *****
(5.00, 1 rating)
Aric Whitewood details WilmotML's research on the application of AI to investment management and offers an overview of the company's prediction engine, GAIA (the Global AI Allocator), which has been running in production since January 2018. Read more.
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4:05pm4:45pm Thursday, April 18, 2019
Location: Trianon Ballroom
Secondary topics:  Automation in machine learning and AI, Media, Marketing, Advertising, Models and Methods, Text, Language, and Speech
Jaewon Lee (Naver/LINE), Sihyeung Han (Naver/LINE)
Jaewon Lee and Sihyeung Han walk you through implementing a self-trained dialogue model using AutoML and the Chatbot Builder Framework. You'll discover the value of AutoML, which allows you to provide better model, and learn how AutoML can be applied in different areas of NLP, not just for chatbots. Read more.
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4:55pm5:35pm Thursday, April 18, 2019
Location: Rendezvous
Secondary topics:  Data and Data Networks
Dmitry Petrov (Iterative AI), Ivan Shcheklein (Iterative AI)
Average rating: ****.
(4.67, 3 ratings)
ML model and dataset versioning is an essential first step in the direction of establishing a good process. Dmitry Petrov and Ivan Shcheklein explore open source tools for ML models and datasets versioning, from traditional Git to tools like Git-LFS and Git-annex and the ML project-specific tool Data Version Control or DVC.org. Read more.