The evolution of TensorFlow and of machine learning infrastructure

The Data Exchange Podcast: Rajat Monga on TensorFlow 2.0, TFX, and the state of machine learning platforms.


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In this episode of the Data Exchange I speak with Rajat Monga, one of the founding members of the TensorFlow Engineering team. Up until recently Rajat was the engineering manager for TensorFlow at Google.

Ray Summit has been postponed until the Fall. In the meantime, enjoy an amazing series of virtual conferences beginning in mid May on the theme “Scalable machine learning, scalable Python, for everyone”. Go to anyscale.com/events for details.

Our conversation spanned many topics, including:

  • TFX, a production scale machine learning platform based on TensorFlow.
  • Distributed training
  • MLIR (Multi-Level Intermediate Representation), “a representation format and library of compiler utilities that sits between the model representation and low-level compilers/executors that generate hardware-specific code.”
  • Deep learning in the enterprise.
  • The state of machine learning infrastructure.

An essential component of this podcast is getting listeners involved.  This week, I want to ask a question related to data types. Specifically, I’d like to know if you are beginning to work with images, video, and audio in your ML applications. I’d love to have you weigh in by filling out the form below. I’ll report on result in a future episode of this podcast:

[Survey question can also be found here: https://bit.ly/2qCVSwM]

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The 2020 Emerging jobs report from Linkedin: Data and AI figure prominently in this annual spotlight of the fastest-growing jobs in nine countries.

Mapping global AI communities: The US, China, and Israel stand out in this new study from Tech Nation and data.world.

[Image from Pixabay: Neurons Braid Network Free]