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: Platforms and infrastructure sessions

Add to your personal schedule
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.
Add to your personal schedule
1:00pm1:40pm Wednesday, April 17, 2019
Implementing AI
Location: Rendezvous
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.
Add to your personal schedule
1:00pm1:40pm Wednesday, April 17, 2019
Case Studies, Machine Learning
Location: Sutton South
Twitter is a company with massive amounts of data. Thus, it is no wonder that the company applies machine learning in myriad of ways. In this session, we are going to describe, in depth, one of those use cases: Timeline Ranking. From modeling to infrastructure our goal is to share some of the optimizations that this team have made in order to have models that are both expressive and efficient. Read more.
Add to your personal schedule
1:50pm2:30pm Wednesday, April 17, 2019
Implementing AI
Location: Rendezvous
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.
Add to your personal schedule
1:50pm2:30pm Wednesday, April 17, 2019
Implementing AI
Location: Mercury Rotunda
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.
Add to your personal schedule
1:50pm2:30pm Wednesday, April 17, 2019
Models and Methods
Location: Grand Ballroom West
vinay rao (RocketML Inc), Santi Adavani (Mr)
Growing data sizes are causing Training times to increase 5x the Moore's law. While Hardware innovations are helping, new software architectures for distributed system are needed for AI industry to solve critical problems. RocketML results show that we can build logistic regression models on KDD12 data set with ~150 Million samples on 8 Intel Xeon node cluster in < 1 minute. Read more.
Add to your personal schedule
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.
Add to your personal schedule
4:05pm4:45pm Wednesday, April 17, 2019
Sarah Aerni (Salesforce Einstein)
How does Salesforce manage to make data science an agile partner to over 100,000 customers? We will share the nuts and bolts of the platform and our agile process. From our open-source autoML library (TransmogrifAI) and experimentation to deployment and monitoring, we will cover how the tools make it possible for our data scientist to rapidly iterate and adopt a truly agile methodology. Read more.
Add to your personal schedule
4:55pm5:35pm Wednesday, April 17, 2019
AI Business Summit, Case Studies
Location: Sutton North/Center
Scott Clark (SigOpt)
Increasingly, companies building modeling platforms to empower their researchers to efficiently scale the development and productionalization of their models. During this talk, we use a case study from a leading algorithmic trading firm to draw general best practices for building these types of platforms in any industry. Read more.
Add to your personal schedule
4:55pm5:35pm Wednesday, April 17, 2019
Implementing AI
Location: Rendezvous
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.
Add to your personal schedule
9:30am9:50am Thursday, April 18, 2019
Implementing AI
Location: Grand Ballroom West
Applied Machine Learning at Facebook Read more.
Add to your personal schedule
11:05am11:45am Thursday, April 18, 2019
Implementing AI
Location: Mercury Rotunda
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.
Add to your personal schedule
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.