ML Pipelines on Google Cloud

In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX).
Course info
Level
Advanced
Updated
Feb 18, 2021
Duration
3h 4m
Table of contents
Introduction
Introduction to TFX Pipelines
Pipeline orchestration with TFX
Custom components and CI/CD for TFX pipelines
ML Metadata with TFX
Continuous Training with multiple SDKs, KubeFlow & AI Platform Pipelines
Continuous Training with Cloud Composer
ML Pipelines with MLflow
Summary
Description
Course info
Level
Advanced
Updated
Feb 18, 2021
Duration
3h 4m
Description

In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. You will also learn how you can automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata. Then we will change focus to discuss how we can automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use another tool on Google Cloud, Cloud Composer, to orchestrate your continuous training pipelines. And finally, we will go over how to use MLflow for managing the complete machine learning life cycle.

About the author
About the author

Build, innovate, and scale with Google Cloud Platform.

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