All times are Pacific Standard Time (UTC-08:00).
Sessions will be available through Livestream during the event, and available after the event on the TensorFlow YouTube Channel.

Day 1 - March 11, 2020

8:00 AM Badge Pick-up & Breakfast
9:30 AM Keynote Megan Kacholia

Kemal El Moujahid

Manasi Joshi
9:50 AM Learning to Read with TensorFlow and Keras

Natural Language Processing (NLP) has hit an inflection point, and this talk shows you how TensorFlow and Keras make it easy to preprocess, train, and hypertune text models.

Karmel Allison
10:10 AM TensorFlow Hub: Making Model Discovery Easy

TF Hub is the main repository for ML models. This talk looks into all the new features and how they can make your model discovery journey even better.

Luis Gustavo Martins
10:20 AM Collaborative ML with TensorBoard.dev

Sharing experiment results is an important part of the ML process. This talk shows how TensorBoard.dev can enable collaborative ML by making it easy to share experiment results in your paper, blog post, social media, and more.

Gal Oshri
10:27 AM Transitioning Kagglers to TPU with TF 2.x

Recently, Kaggle introduced TPU support through its competition platform. This talk touches on how Kaggler competitors transitioned from GPU to TPU use, first in Colab, and then in Kaggle notebooks.

Phil Culliton
10:32 AM An End-to-End Journey through the TensorFlow Ecosystem Hannes Hapke

Catherine Nelson
10:42 AM Performance Profiling in TF 2

This talk presents a profiler that Google internally uses to investigate TF performance on platforms including GPU, TPU, and CPU.

CK Luk

Qiumin Xu
10:52 AM Break
11:52 AM Research with TensorFlow Alexandre Passos
12:10 PM Differentiable Convex Optimization Layers

Convex optimization problems are used to solve many problems in the real world. Until now, it has been difficult to use them in TensorFlow pipelines. This talk presents cvxpylayers, a package that makes it easy to embed convex optimization problems into TensorFlow, letting you tune them using gradient descent.

Akshay Agrawal, Stanford University
12:15 PM Scaling Tensorflow Data Processing with tf.data

As model training becomes more distributed in nature, tf.data has evolved to be more distribution aware and performant. This talk presents tf.data tools for scaling TensorFlow data processing. In particular: tf.data service that allows your tf.data pipeline to run on a cluster of machines, and tf.data.snapshot that materializes the results to disk for reuses across multiple invocations.

Rohan Jain
12:30 PM Scaling TensorFlow 2 Models to Multi-Worker GPUs

This talk showcases multiple performance improvements in TensorFlow 2.2 to accelerate and scale users' ML training workload to multi-worker multi-GPUs. We walk through the optimizations using a BERT fine-tuning task in TF model garden, written using a custom training loop.

Zongwei Zhou
12:45 PM Making the Most of Colab

Learn tips and tricks from the Colab team. This talk describes how TensorFlow users make the most of Colab, and peeks behind the curtain to see how Colab works.

Timothy Novikoff
12:50 PM Lunch at Google Event Center (MP7)
2:20 PM MLIR: Accelerating TF with Compilers Tatiana Shpeisman
2:30 PM TFRT: A New TensorFlow Runtime

TFRT is a new runtime for TensorFlow. Leveraging MLIR, it aims to provide a unified infrastructure layer across server and mobile workloads, with pluggable support for diverse accelerators in a single system. This approach provides efficient use of the multithreaded host CPUs, supports fully asynchronous programming models, and focuses on low-level efficiency.

Mingsheng Hong
2:40 PM TFX: Production ML with TensorFlow in 2020 Tris Warkentin

Zhitao Li

Marcel Rummens
3:05 PM TensorFlow Enterprise: Productionizing TensorFlow with Google Cloud

The hardest part of ML adoption in enterprise is Productization. As we have seen in recent discussions around "ML Ops", there are many gaps between Data Scientists' PoC Notebook and a production ML system manageable by an Ops team. This talk shows us how TensorFlow Enterprise solves these problems with Google Cloud for productionizing your TensorFlow code for mission-critical business operation.

Kaz Sato
3:15 PM TensorFlow Lite: ML for Mobile and IoT Devices

Learn about how to deploy ML to mobile phones and embedded devices. Now deployed on billions of devices in production - its the world best cross-platform ML framework for mobile and microcontrollers. Tune in for our new exciting announcements.

Tim Davis

T.J. Alumbaugh
3:35 PM TensorFlow.js: Machine Learning for the Web and Beyond

TensorFlow.js is a platform for training and deploying machine learning models in browsers, or anywhere Javascript can run, such as mobile devices, WeChat mini app platform, and Raspberry Pi. It provides several back ends, including a CPU, GPU, Node, and WASM back end. It also provides a collection of pretrained models, including the two newest additions: MobileBERT and FaceMesh.

Na Li
3:45 PM Break
4:45 PM Getting Involved in the TF Community

Learn how you can be a part of the growing TensorFlow ecosystem and become a contributor through code, documentation, education, or community leadership.

Joana Carraqueira
4:55 PM Responsible AI with TensorFlow: Fairness and Privacy Tulsee Doshi

Miguel Guevara
5:40 PM Social Hour
Please note that the majority of demos and all office hours will only be available on Day 1.
Schedule is subject to change.