Video description
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 and Charles Chen (Google) 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.
Prerequisite knowledge
- 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
Table of contents
Product information
- Title: TFX: Production ML pipelines with TensorFlow
- Author(s):
- Release date: February 2020
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 0636920373841
You might also like
book
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. …
book
Building Machine Learning Pipelines
Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t …
book
Kubeflow for Machine Learning
If you’re training a machine learning model but aren’t sure how to put it into production, …
book
Data Science from Scratch, 2nd Edition
To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, …