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: Deep Learning and Machine Learning tools sessions

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9:00am - 5:00pm Monday, April 15 & Tuesday, April 16
Location: Harlem
Ana Hocevar (The Data Incubator)
PyTorch is a machine learning library for Python that allows users to build deep neural networks with great flexibility. Its easy to use API and seamless use of GPUs make it a sought-after tool for deep learning. This course will introduce the PyTorch workflow and demonstrate how to use it. Students will be equipped with the knowledge to build deep learning models using real-world datasets. Read more.
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9:00am - 5:00pm Monday, April 15 & Tuesday, April 16
Implementing AI
Location: Madison
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
Implementing AI, Models and Methods
Location: Green Room
Francesca Lazzeri (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
Implementing AI, Models and Methods
Location: Midtown Suite
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
Implementing AI
Location: Mercury Rotunda
Mo Patel (Independent)
From social network photo filters to self-driving cars, computer vision has brought applied deep learning to the masses. Built by the pioneers of computer vision software, PyTorch enables developers to rapidly build computer vision models. Mo Patel offer an overview of computer vision fundamentals and walk you through using PyTorch to build computer vision applications. Read more.
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9:00am12:30pm Tuesday, April 16, 2019
Models and Methods
Location: Beekman
Gunnar Carlsson (Ayasdi)
Using Topological Data Analysis, one can describe the functioning and learning of a neural network in a compact and understandable way. This understanding results in material speedups in performance (training time + accuracy) and allows for data-type customization of neural network architectures to further boost performance and widen the applicability of the method to all data sets. Read more.
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9:00am12:30pm Tuesday, April 16, 2019
Implementing AI
Location: Regent Parlor
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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1:45pm5:15pm Tuesday, April 16, 2019
Implementing AI
Location: Regent Parlor
David Arpin (Amazon Web Services)
Learn how to use the Amazon SageMaker platform to build a machine learning model to recommend products to customers based on their past preferences. Read more.
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1:45pm5:15pm Tuesday, April 16, 2019
Implementing AI
Location: Mercury Rotunda
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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1:45pm5:15pm Tuesday, April 16, 2019
Location: Beekman
Robert Nishihara (UC Berkeley), Philipp Moritz (UC Berkeley), Ion Stoica (UC Berkeley)
Ray is a general purpose framework for programming your cluster. We will lead a deep dive into Ray, walking you through its API and system architecture and sharing application examples, including several state-of-the-art AI algorithms. Read more.
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11:05am11:45am Wednesday, April 17, 2019
Implementing AI
Location: Rendezvous
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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11:05am11:45am Wednesday, April 17, 2019
Implementing AI
Location: Mercury Rotunda
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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1:00pm1:40pm Wednesday, April 17, 2019
Implementing AI
Location: Mercury Rotunda
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
Machine Learning, Models and Methods
Location: Grand Ballroom West
Ameet Talwalkar (Carnegie Mellon University and Determined AI)
Hyperparameter tuning is a crucial, yet expensive, component of the ML development lifecycle. Given the growing costs of model training, we would like to leverage parallelism to tune models in roughly the same wall-clock time needed to train a single model. We propose an elegant solution to this problem, and present extensive experimental results supporting the effectiveness of our approach. Read more.
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2:40pm3:20pm Wednesday, April 17, 2019
Implementing AI
Location: Rendezvous
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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2:40pm3:20pm Wednesday, April 17, 2019
Interacting with AI
Location: Regent Parlor
Matt Zeiler (Clarifai)
At the core of today's problems with image classification and deep learning lies one fundamental truth: most AI systems operate by choosing the path of least resistance – not the path of highest long-term quality. Matt Zeiler, founder and CEO of Clarifai, will discuss the company's approach to Closing the Loop on AI and employing techniques to counter the AI quality regression phenomenon. Read more.
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4:05pm4:45pm Wednesday, April 17, 2019
Machine Learning, Models and Methods
Location: Grand Ballroom West
Bichen Wu (UC Berkeley)
For years we have been designing neural networks manually, but such design flow is extremely inefficient and designed networks are sub-optimal. To address this, we introduce an automated framework for neural network design and optimization. This approach generates superior neural network design and greatly reduces the need for manual efforts. Read more.
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4:05pm4:45pm Wednesday, April 17, 2019
Implementing AI
Location: Mercury Rotunda
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
Interacting with AI
Location: Regent Parlor
Paris Buttfield-Addison (Secret Lab Pty. Ltd.), Mars Geldard (University of Tasmania), Tim Nugent (lonely.coffee)
Learn how to use Unity to train, explore, and manipulate intelligent agents that learn. Train a quadruped to walk. Then train it to explore, fetch, and manipulate the world. Games are great places to explore AI. They’re wonderful contained problem spaces. Learn how to use them, even though you’re not a game developer. Read more.
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4:55pm5:35pm Wednesday, April 17, 2019
Implementing AI
Location: Mercury Rotunda
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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1:00pm1:40pm Thursday, April 18, 2019
Implementing AI
Location: Mercury Rotunda
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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1:00pm1:40pm Thursday, April 18, 2019
Machine Learning, Models and Methods
Location: Grand Ballroom West
Francesca Lazzeri (Microsoft), Wee Hyong Tok (Microsoft)
Automated machine learning (AutoML) enables both data scientists and domain experts (with limited machine learning training) to be productive and efficient. AutoML is seen as a fundamental shift in which organizations can approach making machine learning. In this talk, you'll learn how to use auto ML to automate selection of machine learning models and automate tuning of hyper-parameters. Read more.
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2:40pm3:20pm Thursday, April 18, 2019
Implementing AI
Location: Mercury Rotunda
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.