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Google

ML Pipelines on Google Cloud - Locales

Google via Google Cloud Skills Boost

This course may be unavailable.

Overview

This course, ML Pipelines on Google Cloud - Locales, is intended for non-English learners only. To take this course in English, please enroll in 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), 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.

Syllabus

  • Introduction
    • Course Introduction
  • Introduction to TFX Pipelines
    • TensorFlow Extended (TFX)
    • TFX concepts
    • TFX standard data components
    • TFX standard model components
    • TFX pipeline nodes
    • TFX libraries
    • Lab Intro: TFX Walkthrough
    • TFX Standard Components Walkthrough
    • Quiz
  • Pipeline orchestration with TFX
    • TFX Orchestrators
    • Apache Beam
    • TFX on Cloud AI Platform
    • Lab Intro: TFX pipelines on Cloud AI Platform
    • TFX on Cloud AI Platform Pipelines
    • Quiz
  • Custom components and CI/CD for TFX pipelines
    • TFX custom components - Python functions
    • TFX custom components - containers + subclassed
    • CI/CD for TFX pipeline workflows
    • Lab Intro: CI/CD lab walkthrough
    • CI/CD for a TFX pipeline
    • Quiz
  • ML Metadata with TFX
    • TFX Pipeline Metadata
    • TFX ML Metadata data model
    • TFX Pipeline Metadata lab intro
    • TFX Metadata
    • Quiz
  • Continuous Training with multiple SDKs, KubeFlow & AI Platform Pipelines
    • Containerized Training Applications
    • Containerizing PyTorch, Scikit, and XGBoost Applications
    • KubeFlow & AI Platform Pipelines
    • Continuous Training
    • Lab Intro : Continuous Training with multiple SDKs
    • Continuous Training with TensorFlow, PyTorch, XGBoost, and Scikit Learn Models with Kubeflow and AI Platform Pipelines
    • Quiz
  • Continuous Training with Cloud Composer
    • 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
    • Lab Intro: Continuous Training Pipelines with Cloud Composer
    • Continuous Training Pipelines with Cloud Composer
    • Quiz
  • ML Pipelines with MLflow
    • 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: MLflow using Databricks Community Edition
    • Quiz
  • Summary
    • Course Summary

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