ML Pipelines on Google Cloud
- Offered byCoursera
ML Pipelines on Google Cloud at Coursera Overview
Duration | 11 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
ML Pipelines on Google Cloud at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 9 of 9 in the Preparing for Google Cloud Certification: Machine Learning Engineer
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Advanced Level
- Approx. 11 hours to complete
- English Subtitles: English
ML Pipelines on Google Cloud at Coursera Course details
- 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 <<<
ML Pipelines on Google Cloud at Coursera Curriculum
Welcome to ML Pipelines on Google Cloud
Course Introduction
[IMPORTANT] : Please Read
How to download course resources
How to Send Feedback
TensorFlow Extended (TFX)
TFX concepts
TFX standard data components
TFX standard model components
TFX pipeline nodes
TFX libraries
Getting Started with Google Cloud and Qwiklabs
Lab Intro: TFX Standard Components Walkthrough
Module Quiz
Pipeline orchestration with TFX
TFX Orchestrators
Apache Beam
TFX on Cloud AI Platform
Lab Intro: TFX on Cloud AI Platform
Module Quiz
TFX custom components - Python functions
TFX custom components - containers & subclassed
CI/CD for TFX pipeline workflows
Lab Intro: CI/CD for TFX Pipelines
Module Quiz
ML Metadata with TFX
TFX Pipeline Metadata
TFX ML Metadata data model
Lab Intro: TFX Pipeline Metadata
Module Quiz
Containerized Training Applications
Containerizing PyTorch, Scikit, and XGBoost Applications
KubeFlow & AI Platform Pipelines
Continuous Training
Lab Intro: Lab Intro: Continuous Training with TensorFlow, PyTorch, XGBoost, and Scikit Learn Models with KubeFlow and AI Platform Pipelines
Module 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
Module Quiz
Introduction
Overview of ML development challenges
How MLflow tackles these challenges
MLflow tracking
MLflow projects
MLflow models
MLflow model registry
Introduction
Demo: Deploying MLflow Locally Tracking Keras, TensorFlow, and Sckit-learn experiments
Module Quiz
Course Summary
ML Pipelines on Google Cloud at Coursera Admission Process
Important Dates
Other courses offered by Coursera
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