Building machine learning models is a multistage process that begins with experimenting with your data and model architectures and extends all the way to large-scale training and serving. TensorFlow’s high-level APIs make this process smooth and easy, whether you are starting small or going big.
Amit Patankar walks you through building, training, and debugging a model and then exporting it for serving using these APIs.
This session is sponsored by Google.
Amit Patankar is an engineer on the TensorFlow developer infrastructure team at Google, where he works on everything from releases and issue management to infrastructure and platform requests. Amit is passionate about the democratization of TensorFlow and is fascinated by the vast potential of applications of AI. Previously, he worked on smart home devices at Nest. Amit holds a degree in electrical engineering and computer science from UC Berkeley.
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