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ML Pipelines on Google Cloud
  • Level Expert
  • Duration 11 hours
  • Course by Google Cloud
  • Offered by

About

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 <<<

Modules

Welcome to the course

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Course Introduction

1 Readings

  • [IMPORTANT] : Please Read

Introduction to TFX Pipelines

    • 6 Videos
    • 1 Assignment
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1 Assignment

  • Module Quiz

6 Videos

  • TensorFlow Extended (TFX)
  • TFX concepts
  • TFX standard data components
  • TFX standard model components
  • TFX pipeline nodes
  • TFX libraries

Pipeline Orchestration with TFX

    • 3 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Module Quiz

3 Videos

  • TFX Orchestrators
  • Apache Beam
  • TFX on Cloud AI Platform

Custom components and CI/CD for TFX pipelines

    • 3 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Module Quiz

3 Videos

  • TFX custom components - Python functions
  • TFX custom components - containers & subclassed
  • CI/CD for TFX pipeline workflows

ML Metadata with TFX

    • 2 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Module Quiz

2 Videos

  • TFX Pipeline Metadata
  • TFX ML Metadata data model

Continuous Training with multiple SDKs, KubeFlow & AI Platform Pipelines

    • 4 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Module Quiz

4 Videos

  • Containerized Training Applications
  • Containerizing PyTorch, Scikit, and XGBoost Applications
  • KubeFlow & AI Platform Pipelines
  • Continuous Training

Continuous Training with Cloud Composer

    • 5 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Module Quiz

5 Videos

  • What is Cloud Composer?
  • Core Concepts of Apache Airflow
  • Continuous Training Pipelines using Cloud Composer : Data
  • Continuous Training Pipelines using Cloud Composer : Model
  • Apache Airflow, Containers, and TFX

ML Pipelines with MLflow

    • 9 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Module Quiz

9 Videos

  • Introduction
  • Overview of ML development challenges
  • How MLflow tackles these challenges
  • MLflow tracking
  • MLflow projects
  • MLflow models
  • MLflow model registry
  • Demo: Deploying MLflow locally
  • Demo: Deploying MLflow Locally Tracking Keras, TensorFlow, and Sckit-learn experiments

Course Summary

    • 1 Videos
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1 Videos

  • Course Summary

Auto Summary

"ML Pipelines on Google Cloud" is an expert-level course designed for advanced learners in Data Science & AI. Taught by Google Cloud's ML Engineers, it delves into TensorFlow Extended (TFX) for managing ML pipelines, continuous integration, and deployment. The course also covers automating and reusing ML pipelines across multiple frameworks like TensorFlow, PyTorch, and Scikit-learn, using tools like Cloud Composer and MLflow. Ideal for those with a solid ML background, the course spans 660 minutes and is available via Coursera with a starter subscription option.

Google Cloud Training
Instructor

Google Cloud Training