October 28–31, 2019
 
Great America Meeting Room 3
Add Production ML pipelines with TensorFlow Extended (TFX) to your personal schedule
9:00am 2-day Training Production ML pipelines with TensorFlow Extended (TFX) Aurélien Géron (Kiwisoft)
Room 203
Add End-to-end machine learning with TensorFlow 2.0 on Google Cloud Platform to your personal schedule
9:00am 2-day Training End-to-end machine learning with TensorFlow 2.0 on Google Cloud Platform Valliappa Lakshmanan (Google)
Room 204
Add Introduction to TensorFlow to your personal schedule
9:00am 2-day Training Introduction to TensorFlow Robert Schroll (The Data Incubator)
Room 211
Add TensorFlow on AWS to your personal schedule
9:00am 2-day Training TensorFlow on AWS Shashank Prasanna (Amazon Web Services), vikrant kahlir (Amazon Web Services), Rama Thamman (Amazon Web Services), Shreyas Subramanian (Amazon)
Room 212
Add Hands-on deep learning with TensorFlow 2.0 and Azure to your personal schedule
9:00am 2-day Training Hands-on deep learning with TensorFlow 2.0 and Azure Maxim Lukiyanov (Microsoft), Vaidyaraman Sambasivam (Microsoft), Mehrnoosh Sameki (MERS) (Microsoft), Santhosh Pillai (Microsoft)
Magnolia
Add Contributor Summit to your personal schedule
9:00am Contributor Summit Edd Wilder-James (Google), Martin Wicke (Google), Omoju Miller (GitHub), Edd Wilder-James (Google), Joana Filipa Bernardo Carrasqueira (Google), Chandni Shah (Google), Jason Zaman (Light Labs), Yifei Feng (Google), Gunhan Gulsoy (Google Brain), Edd Wilder-James (Google), Sean Morgan (Two Six Labs), Jason Zaman (Light Labs), Karmel Allison (Google), Martin Wicke (Google), Margaret Maynard-Reid (Tiny Peppers)
Add Dine-Around to your personal schedule
7:00pm Dine-Around | Room: Various Locations
12:30pm Lunch | Room: Santa Clara Ballroom
8:00am Morning Coffee | Room: Grand Ballroom Foyer
10:30am Morning Break | Room: Grand Ballroom Foyer
3:00pm Afternoon Break | Room: Grand Ballroom Foyer
9:00am-5:00pm (8h)
Production ML pipelines with TensorFlow Extended (TFX)
Aurélien Géron (Kiwisoft)
Aurélien Géron dives into creating production ML pipelines with TensorFlow Extended (TFX) and using TFX to move from ML coding to ML engineering. You'll walk through the basics and put your first pipeline together, then learn how to customize TFX components and perform deep analysis of model performance.
9:00am-5:00pm (8h)
End-to-end machine learning with TensorFlow 2.0 on Google Cloud Platform
Valliappa Lakshmanan (Google)
Valliappa Lakshmanan shows you how to use Google Cloud Platform to design and build machine learning (ML) models and how to deploy them into production. You'll walk through the process of building a complete machine learning pipeline from ingest and exploration to training, evaluation, deployment, and prediction.
9:00am-5:00pm (8h)
Introduction to TensorFlow
Robert Schroll (The Data Incubator)
The TensorFlow library provides for the use of computational graphs with automatic parallelization across resources, ideal architecture for implementing neural networks. Robert Schroll introduces TensorFlow's capabilities in Python, moving from building machine learning algorithms piece by piece to using the Keras API provided by TensorFlow with several hands-on applications.
9:00am-5:00pm (8h)
TensorFlow on AWS
Shashank Prasanna (Amazon Web Services), vikrant kahlir (Amazon Web Services), Rama Thamman (Amazon Web Services), Shreyas Subramanian (Amazon)
Amazon Web Services (AWS) offers a breadth and depth of services to easily build, train, and deploy TensorFlow models. Shashank Prasanna, Vikrant Kahlir, and Rama Thamman give you hands-on experience working with these services.
9:00am-5:00pm (8h)
Hands-on deep learning with TensorFlow 2.0 and Azure
Maxim Lukiyanov (Microsoft), Vaidyaraman Sambasivam (Microsoft), Mehrnoosh Sameki (MERS) (Microsoft), Santhosh Pillai (Microsoft)
Maxim Lukiyanov, Vaidyaraman Sambasivam, Mehrnoosh Samekihow, and Santhosh Pillai demonstrate how AzureML helps data scientists be more productive when working through developing TensorFlow models for production. You'll see the whole model development lifecycle from training to deployment and ML ops to model interpretability.
9:00am-5:00pm (8h)
Contributor Summit
Edd Wilder-James (Google), Martin Wicke (Google), Omoju Miller (GitHub), Edd Wilder-James (Google), Joana Filipa Bernardo Carrasqueira (Google), Chandni Shah (Google), Jason Zaman (Light Labs), Yifei Feng (Google), Gunhan Gulsoy (Google Brain), Edd Wilder-James (Google), Sean Morgan (Two Six Labs), Jason Zaman (Light Labs), Karmel Allison (Google), Martin Wicke (Google), Margaret Maynard-Reid (Tiny Peppers)
Contributor Summit
7:00pm-9:00pm (2h)
Dine-Around
Get to know your fellow attendees over dinner. We've made reservations for you at some of the most sought-after restaurants in town. This is a great chance to make new connections and sample some of the great cuisine Santa Clara has to offer.
12:30pm-1:30pm (1h)
Break: Lunch
8:00am-9:00am (1h)
Break: Morning Coffee
10:30am-11:00am (30m)
Break: Morning Break
3:00pm-3:30pm (30m)
Break: Afternoon Break

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