The TensorFlow Dev Summit was on March 6-7, 2019. Not everyone got to attend in person so we are organizing this Extended event with Summit highlights, reprise talks and community discussions.
You will hear all the big announcements from the Summit, learn what is new with TensorFlow 2.0 and how to contribute to TensorFlow - open source and the most popular ML framework.
Bonus: you will also get new stickers of TensorFlow and Keras!
6:00PM Check-in & food/drinks
6:45PM Learnings from TensorFlow Dev Summit, Margaret Maynard-Reid
7:00PM End to end ML: TensorFlow Extended (TFX) & KubeFlow, Amy Unruh
7:30PM TensorFlow Federated: Machine Learning on Decentralized Data (TFF), Emily Glanz
8:00PM Q&A, community discussion
Learnings from TensorFlow Dev Summit
I will share with you learnings from the Summit including TF 2.0 alpha, tf.Keras and how to get started, migrating to TF 2.0, on-device ML with TensorFlow Lite, Swift for TensorFlow, how to contribute to TensorFlow etc....
Margaret Maynard-Reid is a Google Developer Expert(GDE) for Machine Learning, and she develops Android apps with intelligence. She leads GDG Seattle and co-organizes Seattle Data/Analytics/Machine Learning meetup groups. She writes blogs and speaks at conferences on TensorFlow, deep learning and Android. She is passionate about community building and helping others get into Artificial Intelligence and Machine Learning.
TensorFlow Extended (TFX) & KubeFlow
The open-source TFX libraries are derived from a Google-production-scale machine learning platform. They provide a configuration framework and shared libraries to integrate common components needed to define, launch, and monitor your machine learning system. We'll give an overview of the TFX libraries, with a focus on some of the recently-released ones, and show how you can use the TFX components in Kubeflow Pipelines ML workflows.
Amy Unruh is a developer programs engineer for the Google Cloud Platform, where she focuses on machine learning and data analytics as well as other Cloud Platform technologies. Amy has an academic background in CS/AI and has also worked at several startups, industrial R&D, and published a book on App Engine.
TensorFlow Federated: Machine Learning on Decentralized Data (TFF)
Learn about federated learning and how TensorFlow Federated can enable researchers and enthusiasts to simulate this type of decentralized machine learning on their own datasets.
Emily Glanz is a software engineer at Google! working on TensorFlow Federated. Before Google, she studied Electrical Engineering at the University of Iowa (Go Hawkeyes!). In her free time she likes making random things (like AIY Furbies), running, and camping.