Course Summary
In this course, you will learn how to build and deploy machine learning pipelines on Google Cloud Platform. Through hands-on exercises, you will gain a better understanding of how to implement different machine learning models and how to use Google Cloud tools to develop and deploy machine learning models.Key Learning Points
- Gain in-depth knowledge of building and deploying machine learning pipelines on Google Cloud Platform
- Learn how to use Google Cloud tools to develop and deploy machine learning models
- Understand how to implement different machine learning models
Related Topics for further study
Learning Outcomes
- Develop and deploy machine learning models on Google Cloud Platform
- Implement different machine learning models
- Build and optimize data pipelines
Prerequisites or good to have knowledge before taking this course
- Basic understanding of machine learning concepts
- Familiarity with Python programming
Course Difficulty Level
IntermediateCourse Format
- Online self-paced course
- Hands-on exercises
- Video lectures
- Quizzes and assessments
Similar Courses
- Applied Data Science with Python
- Introduction to Machine Learning with Python
- Data Engineering on Google Cloud Platform
Related Education Paths
- Google Cloud Certified - Professional Data Engineer
- Google Cloud Certified - Professional Machine Learning Engineer
Notable People in This Field
- Andrew Ng
- Jeff Dean
- Fei-Fei Li
Related Books
Description
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.
Outline
- Welcome to ML Pipelines on Google Cloud
- Course Introduction
- [IMPORTANT] : Please Read
- How to download course resources
- How to Send Feedback
- Introduction to TFX Pipelines
- 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
- 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 for TFX Pipelines
- Module Quiz
- ML Metadata with TFX
- TFX Pipeline Metadata
- TFX ML Metadata data model
- Lab Intro: TFX Pipeline Metadata
- Module 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: 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
- ML Pipelines with MLflow
- 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
- Summary
- Course Summary
Summary of User Reviews
Learn about machine learning pipelines on Google Cloud with this Coursera course. Users have found the course to be informative and well-structured, with practical exercises that help reinforce concepts.Key Aspect Users Liked About This Course
The practical exercises are a key aspect that many users found helpful.Pros from User Reviews
- The course is well-structured and easy to follow
- The practical exercises help reinforce concepts
- The instructors are knowledgeable and engaging
- The course covers a wide range of topics
- The course uses real-world examples
Cons from User Reviews
- The course may be too basic for some users
- The course could benefit from more in-depth explanations
- Some users found the course to be too focused on Google Cloud
- Some users found the course to be too short
- Some users found the course to be too theoretical
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