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Google Cloud

ML Pipelines on Google Cloud

Google Cloud via Coursera

Overview

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. 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. >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service

Syllabus

  • Welcome to ML Pipelines on Google Cloud
    • This module introduces the course and shares the course outline
  • Introduction to TFX Pipelines
    • This module introduces TensorFlow Extended or TFX and covers TFX concepts and components
  • Pipeline orchestration with TFX
    • In this module, you will learn to use the TFX CLI to deploy TFX Pipelines
  • Custom components and CI/CD for TFX pipelines
    • In this module, you will learn to develop a CI/CD workflow to deploy TFX pipelines
  • ML Metadata with TFX
    • This module talks about using TFX Metadata for artifact management
  • Continuous Training with multiple SDKs, KubeFlow & AI Platform Pipelines
    • This module covers continuous training with multiple SDKs, KubeFlow & AI Platform Pipelines
  • Continuous Training with Cloud Composer
    • This module covers continuous training with Cloud Composer
  • ML Pipelines with MLflow
    • This module introduces MLflow and its components
  • Summary
    • This module covers a recap of the course

Taught by

Ajay C Hemnani and Google Cloud Training

Reviews

3.2 rating at Coursera based on 85 ratings

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