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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 Tensor Flow Extended (or TFX), which is Googles production machine learning platform based on Tensor Flow 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...
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
Google Cloud via Coursera
4 hours 30 minutes
Paid Certificate Available
English
On-Demand
Advanced
Instructor
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