Production ML pipelines with TensorFlow Extended (TFX)
What you'll learn, and how you can apply it
- Learn to use TFX to create production ML pipelines
Who is this presentation for?
- You're a DevOps or ML ops engineer, and you need to create and maintain a production ML platform.
- You're a researcher working with large datasets, and you need consistency and repeatability for resource-intensive workloads.
- A working knowledge of machine learning and software development in Python
Hardware and/or installation requirements:
- A Linux or macOS laptop, or access to a remote system
- Issues and approaches in production software deployments and machine learning
- TFX basic overview: Libraries, components, and metadata
- Hands-on workshop: Your first pipeline—TFX on-premises with Airflow
- Hands-on exercise: Developing custom components
- Hands-on workshop: Alternate pipelines—A/B testing pipeline architecture
- Data wrangling with TFX: Finding and fixing problems with TensorFlow Data Validation (TFDV)
- Hands-on exercise: Model understanding problem—TensorFlow Model Analysis (TFMA) and What-if
- Hybrid cloud and on-premises deployments
About your instructor
Aurélien Géron is a machine learning consultant at Kiwisoft and author of the best-selling O’Reilly book Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow. Previously, he led YouTube’s video classification team, was a founder and CTO of Wifirst, and was a consultant in a variety of domains: finance (JPMorgan and Société Générale), defense (Canada’s DOD), and healthcare (blood transfusion). He also published a few technical books (on C++, WiFi, and internet architectures), and he’s a lecturer at the Dauphine University in Paris. He lives in Singapore with his wife and three children.
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