Presented By
O’Reilly + Intel AI
Put AI to Work
April 15-18, 2019
New York, NY

Schedule: Automation in machine learning and AI sessions

Add to your personal schedule
1:00pm1:40pm Wednesday, April 17, 2019
Machine Learning, Models and Methods
Location: Grand Ballroom West
Ameet Talwalkar (Carnegie Mellon University and Determined AI)
Neural architecture search (NAS) is a promising research direction that has the potential to replace expert-designed networks with learned, task-specific architectures. In this talk, we present work which aims to help ground the empirical results in this field. In this talk we propose new NAS baselines. Read more.
Add to your personal schedule
2:40pm3:20pm Wednesday, April 17, 2019
Machine Learning, Models and Methods
Location: Grand Ballroom West
Sanjay Krishnan (University of Chicago)
I use my work over the last few years on building and deploying an RL-based relational query optimizer, a core component of almost every database system, as an exemplary application that highlights some of the under-appreciated challenges in Deep RL practice. 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), Matt Greenwood (Two Sigma Investments)
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
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
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.
Add to your personal schedule
4:05pm4:45pm Thursday, April 18, 2019
Paco Nathan (derwen.ai)
Data governance is an almost overwhelming topic. This talk surveys history, themes, plus a survey of tools, process, standards, etc. Mistakes imply data quality issues, lack of availability, and other risks that prevent leveraging data. OTOH, compliance issues aim to preventing risks of leveraging data inappropriately. Ultimately, risk management plays the "thin edge of the wedge" in enterprise. Read more.
Add to your personal schedule
4:05pm4:45pm Thursday, April 18, 2019
Implementing AI
Location: Rendezvous
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.