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.
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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.
Please take note that this is an advanced level course and to get the most out of this course, ideally you have the following prerequisites:
- You have a good ML background and have been creating/deploying ML pipelines
- You have completed the courses in the ML with Tensorflow on GCP specialization (or at least a few courses)
- You have completed the MLOps Fundamentals course.
Syllabus
WEEK 1
Welcome to ML Pipelines on Google Cloud
This module introduces the course and shares the course outline
Introduction to TFX Pipelines
WEEK 2
Pipeline orchestration with TFX
Custom components and CI/CD for TFX pipelines
WEEK 3
ML Metadata with TFX
Continuous Training with multiple SDKs, KubeFlow & AI Platform Pipelines
WEEK 4
Continuous Training with Cloud Composer
ML Pipelines with MLflow
Summary
