TFX: Production ML pipelines with TensorFlow
Who is this presentation for?
- Data scientists, ML engineers, researchers, and ML ops and DevOps practitioners
ML development often focuses on metrics, delaying work on deployment and scaling issues. ML development designed for production deployments typically follows a pipeline model with scaling and maintainability as inherent parts of the design. Robert Crowe takes a deep dive into TensorFlow Extended (TFX), the open source version of the ML infrastructure platform that Google has developed for its own production ML pipelines.
- Experience with ML development and software development
What you'll learn
- Discover issues and best practices for putting machine learning models and applications into production
Robert Crowe is a data scientist at Google with a passion for helping developers quickly learn what they need to be productive. A TensorFlow addict, he’s used TensorFlow since the very early days and is excited about how it’s evolving quickly to become even better than it already is. Previously, Robert led software engineering teams for large and small companies, always focusing on clean, elegant solutions to well-defined needs. In his spare time, Robert sails, surfs occasionally, and raises a family.
Charles Chen is a senior software engineer at Google on the Tensorflow Extended (TFX) team. He previously worked on Google Cloud Dataflow and Apache Beam. Prior to Google, he earned his bachelor’s and master’s degrees in Computer Science from Stanford University.
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