TFX: Production ML pipelines with TensorFlow
Who is this presentation for?
- Data scientists, machine learning engineers, ML Ops, ML management, and DevOps
Putting machine learning models into production is now mission critical for every business—no matter what size.
TensorFlow is the industry-leading platform for developing, modeling, and serving deep learning solutions. But putting together a complete pipeline for deploying and maintaining a production application of AI and deep learning is much more than training a model. Google has taken its years of experience in developing production ML pipelines and offered the open source community TensorFlow Extended (TFX), an open source version of the tools and libraries that Google uses internally.
Robert Crowe walks you through working code in an example pipeline so you can learn what’s involved in creating a production pipeline. You’ll be able take what you learn and get started on creating your own pipelines for your applications.
- Experience with implementing (or need to implement) a production ML solution
- Familiarity with ML
What you'll learn
- Understand production ML pipelines, the components of TFX pipelines, and issues in ML production deployments
- Gain an introduction to the function and implementation of components
Robert Crowe is a data scientist and TensorFlow Developer Advocate at Google with a passion for helping developers quickly learn what they need to be productive. 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 deployed production ML applications and 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.
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