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.
Author Name: Google Cloud
Author Description:
Google Cloud can help solve your toughest problems and grow your business. With Google Cloud, their infrastructure is your infrastructure. Their tools are your tools. And their innovations are your innovations.

Table of Contents

• Introduction
  2mins
• Introduction to TFX Pipelines
  49mins
• Introduction
  2mins
• Pipeline orchestration with TFX
  25mins
• Introduction to TFX Pipelines
  45mins
• Custom components and CI/CD for TFX pipelines
  25mins
• Pipeline orchestration with TFX
  20mins
• ML Metadata with TFX
  9mins
• Custom components and CI/CD for TFX pipelines
  23mins
• Continuous Training with multiple SDKs, KubeFlow & AI Platform Pipelines
  8mins
• ML Metadata with TFX
  9mins
• Continuous Training with Cloud Composer
  27mins
• Continuous Training with multiple SDKs, KubeFlow & AI Platform Pipelines
  8mins
• ML Pipelines with MLflow
  35mins
• Continuous Training with Cloud Composer
  26mins
• Summary
  1min
• ML Pipelines with MLflow
  35mins
• Summary
  1min