Put AI to work
June 26-27, 2017: Training
June 27-29, 2017: Tutorials & Conference
New York, NY

Schedule: Cloud sessions

The advent of the cloud has made it easier than ever to use AI technology. These sessions highlight the accessibility of both software and hardware infrastructure for AI development.

Add to your personal schedule
9:00am12:30pm Tuesday, June 27, 2017
Implementing AI
Location: Sutton Center Level: Intermediate
Joseph Spisak (Amazon), Sunil Mallya (Amazon Web Services)
Joseph Spisak and Sunil Mallya offer an introduction to the powerful and scalable deep learning framework Apache MXNet. You'll gain hands-on experience using Apache MXNet with preconfigured Deep Learning AMIs and CloudFormation Templates to help speed your development and leave able to quickly spin up AWS GPU clusters to train at record speeds. Read more.
Add to your personal schedule
9:00am12:30pm Tuesday, June 27, 2017
Implementing AI
Location: Murray Hill E/W Level: Advanced
Yufeng Guo (Google), Amy Unruh (Google)
Average rating: **...
(2.00, 2 ratings)
Amy Unruh and Yufeng Guo walk you through training and deploying a machine learning system using TensorFlow, a popular open source library. Amy and Yufeng begin by giving an overview of TensorFlow and demonstrating some fun, already-trained TensorFlow models. Then, they show how to build a simple classifier in TensorFlow, before introducing some more complex classifier models. Read more.
Add to your personal schedule
1:45pm2:25pm Wednesday, June 28, 2017
Implementing AI
Location: Grand Ballroom West Level: Intermediate
Guy Ernest (Amazon Web Services)
Average rating: **...
(2.00, 1 rating)
AWS is democratizing AI, helping you build deep learning systems in any scale, in any team size and skill, and for every use case. Guy Ernest discusses the state of deep learning, the tools that can take advantage of its power, and best practices for building successful businesses in the cloud, including data handling, models learning, deployment, and integration to other parts of the business. Read more.
Add to your personal schedule
4:00pm4:40pm Wednesday, June 28, 2017
Implementing AI
Location: Grand Ballroom West Level: Intermediate
Yufeng Guo (Google)
Average rating: ****.
(4.00, 2 ratings)
Moving the heavy lifting of machine learning to the cloud is a great way to get large speed-ups. Yufeng Guo walks you through this process in detail so that you'll be ready to scale your own training and prediction services. Read more.
Add to your personal schedule
4:50pm5:30pm Wednesday, June 28, 2017
Interacting with AI
Location: Murray Hill E/W Level: Intermediate
Brad Abrams (Google)
Average rating: *....
(1.25, 4 ratings)
Brad Abrams explores the latest design and development techniques for building natural language interfaces and draws on the Google Assistant, Actions on Google, and API.AI as examples to explore conversational UI best practices. Read more.
Add to your personal schedule
1:45pm2:25pm Thursday, June 29, 2017
Implementing AI
Location: Sutton South/Regent Parlor Level: Intermediate
Reza Zadeh (Stanford | Matroid)
Providing customized computer vision solutions to a large number of users is a challenge. Matroid allows the creation and serving of computer vision models and algorithms, model sharing between users, and serving infrastructure at scale. Reza Zadeh offers an overview of Matroid's pipeline, which uses TensorFlow, Kubernetes, and Amazon Web Services. Read more.
Add to your personal schedule
2:35pm3:15pm Thursday, June 29, 2017
Implementing AI
Location: Sutton South/Regent Parlor Level: Intermediate
Joseph Bradley (Databricks), Xiangrui Meng (Databricks)
Joseph Bradley and Xiangrui Meng share best practices for integrating popular deep learning libraries with Apache Spark, covering cluster setup, data ingest, configuring clusters, and monitoring jobs. Joseph and Xiangrui then demonstrate these techniques using Google’s TensorFlow library. Read more.
Add to your personal schedule
2:35pm3:15pm Thursday, June 29, 2017
Implementing AI
Location: Grand Ballroom West Level: Intermediate
Matt Zeiler (Clarifai)
Average rating: *....
(1.33, 3 ratings)
AI-powered machine learning technologies bring a higher and more complex level of technical debt to applications. Matt Zeiler shares best practices for companies hoping to build AI into their businesses and explores how machine learning increases technical debt, the key contributors, and how to avoid or reduce technical debt related to machine learning. Read more.
Add to your personal schedule
4:00pm4:40pm Thursday, June 29, 2017
Implementing AI
Location: Grand Ballroom West Level: Intermediate
Yonghua Lin (IBM Research)
Yonghua Lin leads a deep dive into AI Vision, a deep learning system from IBM for image and video analysis in both edge and cloud environments, exploring its system design, performance optimization, and large-scale capability for training and inference. Read more.