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.

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 «<

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Syllabus

Syllabus - What you will learn from this course
Week 1
Welcome to ML Pipelines on Google Cloud
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

FAQ

When will I have access to the lectures and assignments?

Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

What will I get if I subscribe to this Certificate?

When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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Last Updated: September 1, 2015

Reviews

This is a great course to learn how to apply MLOps principles in large scale machine learning projects. I'll refer to this course in the near future to bring its concepts to customer ML platforms.