Presented By
O’Reilly + Intel AI
Put AI to Work
April 15-18, 2019
New York, NY
Discover opportunities for applied AI
Organizations that successfully apply AI innovate and compete more effectively. How is AI transforming your business?
Be a part of the program—apply to speak by October 16.

Schedule: Implementing AI sessions

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9:00am - 5:00pm Monday, April 15 & Tuesday, April 16
Location: Madison
Secondary topics:  Deep Learning and Machine Learning tools
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. This training will introduce TensorFlow's capabilities in Python. It will move from building machine learning algorithms piece by piece to using 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: Green Room
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 will 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: Midtown Suite
Secondary topics:  Deep Learning and Machine Learning tools, Models and Methods, Text, Language, and Speech
Delip Rao (R7 Speech Science)
Delip Rao explores 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: Regent Parlor
Secondary topics:  Deep Learning and Machine Learning tools, Ethics, Privacy, and Security
Rachel Bellamy (IBM Research), Kush Varshney (IBM Research), Karthikeyan Natesan Ramamurthy (IBM), Michael Hind (IBM Research AI)
Learn to use and contribute to the new open-source Python package AI Fairness 360 directly from its creators. Architected to translate new developments from research labs to data science practitioners in industry, this is the first comprehensive toolkit with 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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9:00am12:30pm Tuesday, April 16, 2019
Location: Mercury Rotunda
Secondary topics:  Deep Learning and Machine Learning tools
Mo Patel (Independent)
This tutorial will focus on all aspects of the PyTorch lifecycle via hand on examples such as image classification, text classification, and linear modeling. Other aspects of machine learning such as transfer learning, data modeling and deploying to production will be covered via immersive labs. Read more.
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1:45pm5:15pm Tuesday, April 16, 2019
Location: Sutton South
Secondary topics:  Models and Methods, Temporal data and time-series
Bruno Goncalves (JPMorgan Chase & Co.)
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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1:45pm5:15pm Tuesday, April 16, 2019
Location: Mercury Rotunda
Secondary topics:  Deep Learning and Machine Learning tools, Text, Language, and Speech
In this workshop, you will get hands-on experience in developing intelligent AI assistants based entirely on machine learning and using only open source tools - Rasa NLU and Rasa Core. You will learn the fundamentals of conversational AI and the best practices of developing AI assistants that scale and learn from real conversational data. Read more.
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11:05am11:45am Wednesday, April 17, 2019
Location: Mercury Rotunda
Secondary topics:  Deep Learning and Machine Learning tools
joshua gordon (Google)
Learn about the very latest in TensorFlow direct from Google. We will focus on TensorFlow 2.0 and its easy-to-use eager execution. We'll also cover how to use our revised high-level API, and pitfalls and tricks to get performance on accelerator hardware. Read more.
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11:05am11:45am Wednesday, April 17, 2019
Location: Rendezvous
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? This session will help you by providing a step-by-step guide to go from a pre-trained deep learning model, package it in a Docker container and deploy as a webservice on Kubernetes cluster. Read more.
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1:00pm1:40pm Wednesday, April 17, 2019
Location: Mercury Rotunda
Secondary topics:  Deep Learning and Machine Learning tools
Catherine Ordun (Booz Allen Hamilton)
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. This session 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: Rendezvous
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)
Time series database (TSDB) is of great use for data management in IoT, finance, etc. Performance is always a major optimization point for TSDB. Recently, we introduced neural networks and reinforcement learning to perform mode selection for compression algorithm. Experimental results show one can improve average compression ratio by 20%-120%, comparing with other well-known compression format. 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)
Organizations are increasingly overwhelmed with large amounts of streaming data. Today’s approach to processing it 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. Read more.
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1:50pm2:30pm Wednesday, April 17, 2019
Location: Mercury Rotunda
Secondary topics:  Platforms and infrastructure, Text, Language, and Speech
Jeremy Lewi (Google), Hamel Husain (GitHub)
In this talk, we will use the example of a search engine for code using natural language to illustrate how Kubeflow and Kubernetes can be used to build and deploy ML products. Read more.
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1:50pm2:30pm Wednesday, April 17, 2019
Location: Rendezvous
Secondary topics:  Media, Marketing, Advertising, Platforms and infrastructure
YU DONG (Facebook Inc)
An overview of why, what & 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: Mercury Rotunda
Secondary topics:  AI in the Enterprise, Models and Methods, Text, Language, and Speech
Sumeet Vij (Booz Allen Hamilton), Matt Speck (Booz Allen Hamilton)
The session describes the innovative application of Deep Learning to power Cognitive Conversational Agents. By leveraging Transfer Learning and deep pre-trained models for NLP, we show how Chatbots can overcome limitations of limited training datasets. In addition, we will demonstrate how Machine Learning can advances Robotic Process Automation (RPA) from “robotic” to “cognitive” automation Read more.
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2:40pm3:20pm Wednesday, April 17, 2019
Location: Rendezvous
Secondary topics:  Deep Learning and Machine Learning tools, Media, Marketing, Advertising, Platforms and infrastructure
Yi Zhuang (Twitter), Nicholas Leonard (Twitter)
Twitter is a 4000+ employee company with many ML use cases. Historically, there are many different ways to productionize ML at Twitter. In this session, we describe the setup and benefits of a unified ML platform for production, and how Twitter Cortex team brings together users of various ML tools. Read more.
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4:05pm4:45pm Wednesday, April 17, 2019
Location: Mercury Rotunda
Secondary topics:  AI case studies, Deep Learning and Machine Learning tools, Reliability and Safety
Pradip Bose (IBM T. J. Watson Research Center), Augusto Vega (IBM T. J. Watson Research Center), Nandhini Chandramoorthy (IBM T. J. Watson Research Center)
We will describe the fundamentals of a next generation AI research project. It is focused on creating future "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. Software-hardware system architectures are discussed. Read more.
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4:55pm5:35pm Wednesday, April 17, 2019
Location: Mercury Rotunda
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 is also raising new questions about ways to build fairness, interpretability and other moral and ethical values into these systems. Using Jupyter and TensorFlow, this presentation will share hands-on examples that highlight current work and recommended practices towards the responsible development of AI. Read more.
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4:55pm5:35pm Wednesday, April 17, 2019
Location: Rendezvous
Secondary topics:  Computer Vision, Edge computing and Hardware, Platforms and infrastructure
Ted Way (Microsoft Corporation), Aishani Bhalla (Microsoft)
Deep neural networks (DNNs) have enabled breakthroughs in AI. Serving DNNs at scale has been challenging: fast and cheap? Won’t be accurate. Accurate and fast? Won’t be cheap. You’ll learn how Python and TensorFlow can be used to easily train and deploy computer vision models on Intel FPGAs with Azure Machine Learning and Project Brainwave, getting performance such as ResNet 50 in under 2 ms. Read more.
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9:30am9:45am Thursday, April 18, 2019
Location: Grand Ballroom West
Secondary topics:  Platforms and infrastructure
Kim Hazelwood (Facebook)
Applied Machine Learning at Facebook Read more.
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11:05am11:45am Thursday, April 18, 2019
Location: Mercury Rotunda
Secondary topics:  AI in the Enterprise, Automation in machine learning and AI, Platforms and infrastructure
Diego Oppenheimer (Algorithmia)
In this talk, Diego Oppenheimer, CEO of Algorithmia, will draw upon his work with thousands of developers across hundreds of organizations and discuss the tools and processes every business will need 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: Mercury Rotunda
Secondary topics:  Deep Learning and Machine Learning tools, Platforms and infrastructure
Evan Sparks (Determined AI)
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. We describe the key pitfalls associated with fast, repeatable, model development, and what practitioners can do to avoid these and maintain a super-charged AI development workflow. Read more.
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2:40pm3:20pm Thursday, April 18, 2019
Location: Mercury Rotunda
Secondary topics:  Deep Learning and Machine Learning tools
Magnus Hyttsten (Google)
Session description: This session covers how to use TensorFlow effectively in a distributed manner using best-practices. We will cover using TensorFlow's new DistributionStrategy to get easy high-performance training with Keras models (and custom models) on multi-GPU setups as well as multi-node training on clusters with accelerators. Read more.
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2:40pm3:20pm Thursday, April 18, 2019
Location: Rendezvous
Secondary topics:  Computer Vision, Data and Data Networks, Ethics, Privacy, and Security, Reliability and Safety
Ryan Mukherjee (JHU/APL), Neil Fendley (JHU/APL)
While deep learning has led to many advancements in computer vision, most research has focused on ground-based imagery. To address this, we released an ImageNet for satellite imagery called functional Map of the World (fMoW). We present our work building the dataset, running a public prize challenge, and investigating how one might 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)
Our firm focuses on the application of AI to investment management. Topics covered in this presentation include the application of AI to the problem of asset selection, dealing with low signal-to-noise ratios in financial time series data, the development of real-time macroeconomic indicators from social media data, and the use of heterogeneous compute architectures, specifically GPUs and FPGAs. Read more.
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4:05pm4:45pm Thursday, April 18, 2019
Location: Rendezvous
Secondary topics:  Automation in machine learning and AI, Media, Marketing, Advertising, Models and Methods, Text, Language, and Speech
Jaewon Lee (LINE Corp.), Sihyeung Han (NAVER & LINE Corp)
"Until when are you going to cluster queries by yourself to manage large data corpus?" "Until when are you going to tune model hyper parameters by yourself?" I would like to introduce how to implement self-trained dialogue model by using AutoML in Chatbot within our Chatbot Builder Framework. Read more.
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4:55pm5:35pm Thursday, April 18, 2019
Location: Mercury Rotunda
Secondary topics:  Data and Data Networks
Dmitry Petrov (Iterative AI), Ivan Shcheklein (Iterative AI)
Many companies are using machine learning today, ML teams size is growing and complexity of ML project is increasing. Establishing a well define and manageable process become a central issue in this environment. ML models and data set versioning is an essential first step in the direction of establishing a good process. We will discuss open source tools and practices for ML models versioning. Read more.