The Data Exchange Podcast: Rajat Monga on TensorFlow 2.0, TFX, and the state of machine learning platforms.
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.
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]
- Rajat Monga on “Building intelligent applications with deep learning and TensorFlow”
- Ameet Talwalkar on “How to train and deploy deep learning at scale”
- Andrew Feldman on “Specialized hardware for deep learning will unleash innovation”
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[Image from Pixabay: Neurons Braid Network Free]