Amy Unruh walks you through the process of building a complete machine learning pipeline, covering ingest, exploration, training, evaluation, deployment, and prediction. Along the way, Amy explains how to explore and split large datasets correctly using BigQuery and Cloud Datalab.
Third-party libraries used:
Machine learning and TensorFlow
A wide and deep thought experiment
Wide and deep code model
Diving into a lower level of TensorFlow
Creating a simple network by hand
Upgrading the model to a CNN (time permitting)
Wrap-up and Q&A
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 she’s worked at several startups as well as industrial R&D and published a book on App Engine.
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