Schedule: Sponsored sessions

9:40am–9:45am Wednesday, October 30, 2019

Location: Mission City Ballroom

TensorFlow, open source, and IBM (sponsored by IBM)

Frederick Reiss (IBM)

Average rating:

(4.33, 3 ratings)

IBM has a long history of contributing to the open source projects that make the most difference to its clients, and the company has been working to build responsible solutions to enterprise data science problems for many years. Join Frederick Reiss to hear about IBM's role in open source software, TensorFlow, building AI solutions, and what IBM is excited about with this latest (2.0) release. Read more.

10:00am–10:10am Wednesday, October 30, 2019

Location: Mission City Ballroom

Enterprise-ready TensorFlow in the cloud (sponsored by Google Cloud Platform)

Craig Wiley (Google)

Average rating:

(4.00, 2 ratings)

Enterprise adoption of AI placed new expectations on TensorFlow. Craig Wiley details how to maximize your TensorFlow performance and experience in the cloud. You’ll learn how to speed up your software development and ensure the longevity and reliability of your AI-powered enterprise applications. Read more.

11:00am–11:40am Wednesday, October 30, 2019

Location: Grand Ballroom G

Managing the full deployment lifecycle of TensorFlow models with the MLflow Model Registry (sponsored by Databricks)

Clemens Mewald (Databricks)

Average rating:

(5.00, 1 rating)

Clemens Mewald offers an overview of the latest component of MLflow, a model registry that provides a collaborative hub where teams can share ML models, work together from experimentation to online testing and production, integrate with approval and governance workflows, and monitor ML deployments and their performance. Read more.

11:50am–12:30pm Wednesday, October 30, 2019

Location: Grand Ballroom G

Running TensorFlow at scale on GPUs (sponsored by NVIDIA)

Neil Truong (NVIDIA), Kari Briski US (NVIDIA), Khoa Ho (NVIDIA)

Neil Truong, Kari Briski, and Khoa Ho walk you through their experience running TensorFlow at scale on GPU clusters like the DGX SuperPod and the Summit supercomputer. They explore the design of these large-scale GPU systems and detail how to run TensorFlow at scale using BERT and AI plus high-performance computing (HPC) applications as examples. Read more.

1:40pm–2:20pm Wednesday, October 30, 2019

Location: Grand Ballroom G

Trusted AI: Bringing trust back into AI through open source (sponsored by IBM)

Animesh Singh (IBM)

Join Animesh Singh to learn how IBM leverages the power of open source to bring trust back in AI, using popular open source projects for adversarial AI defense and attacks, bias detection and mitigation, and datasets and model explainability. Read more.

2:30pm–3:10pm Wednesday, October 30, 2019

Location: Grand Ballroom G

Maximizing the performance and longevity of your TensorFlow applications on Google Cloud Platform (sponsored by Google Cloud)

Karthik Ramachandran (Google Cloud), Kaz Sato (Google)

Karthik Ramachandran and Kaz Sato take a look at how you can use AI platform notebooks, deep learning virtual machines, and deep learning containers to build TensorFlow applications. You'll learn to maximize TensorFlow performance on Google Cloud by eliminating I/O bottlenecks and some tips and tricks for ensuring the longevity and reliability of your AI-powered enterprise applications. Read more.

9:25am–9:30am Thursday, October 31, 2019

Location: Mission City Ballroom

Accelerating TensorFlow for research and deployment (sponsored by NVIDIA)

Ujval Kapasi (NVIDIA)

Machine learning on NVIDIA GPUs and systems allows developers to solve problems that seemed impossible just a few years ago. Ujval Kapasi explains how software and hardware advances on GPUs impact development efforts across the community, both today and in the future. Read more.

11:00am–11:40am Thursday, October 31, 2019

Location: Grand Ballroom G

Cloud-nativing AI (sponsored by VMware)

Patricia Florissi (Dell EMC)

Patricia Florissi identifies some intrinsic patterns in the anatomy of emerging digital fabrics, including those demanding agility in adapting to change, in dynamically creating connectivity meshes, and in scaling in size and complexity to unprecedented rates. Read more.