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ML Pipelines on Google Cloud

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).

Read more

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).

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.

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What's inside

Syllabus

Introduction
Introduction to TFX Pipelines
Pipeline orchestration with TFX
Custom components and CI/CD for TFX pipelines
Read more
ML Metadata with TFX
Continuous Training with multiple SDKs, KubeFlow & AI Platform Pipelines
Continuous Training with Cloud Composer
ML Pipelines with MLflow
Summary

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Taught by Google Cloud's ML engineers and trainers, indicating high-quality instruction
Covers the latest machine learning pipeline development techniques used at Google Cloud, providing industry-relevant knowledge
Provides hands-on, practical guidance on implementing and automating ML pipelines, enhancing learners' technical skills
Focuses on TensorFlow Extended, a vital tool for managing ML pipelines, giving learners specialized knowledge
Suitable for intermediate learners with some background in ML and pipeline orchestration, ensuring relevance to their learning journey
Requires learners to have a working knowledge of Cloud Composer and MLflow, which could be a barrier for those not familiar with these tools

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in ML Pipelines on Google Cloud with these activities:
Review TFX and pipeline concepts
Ensure a strong foundation of pipeline concepts including TFX for effective course understanding.
Browse courses on TensorFlow Extended
Show steps
  • Review TFX's architecture and components
  • Explore pipeline orchestration using TFX
Refresh foundational machine learning concepts
Revisiting foundational concepts will provide the necessary background knowledge to succeed in this course.
Browse courses on Machine Learning
Show steps
  • Review textbook chapters on machine learning fundamentals
  • Work through online tutorials on supervised and unsupervised learning algorithms
  • Complete practice problems on model evaluation metrics
Get familiar with TensorFlow Extended
Increase knowledge of TensorFlow Extended, the base of the course's curricula.
Browse courses on TensorFlow Extended
Show steps
  • Review TensorFlow Extended documentation
  • Complete the TensorFlow Extended tutorial
26 other activities
Expand to see all activities and additional details
Show all 29 activities
Read and summarize 'Hands-On Machine Learning with TensorFlow'
Provide a foundation for the course by introducing TensorFlow and its use in machine learning.
View Hands-On Machine Learning with Scikit-Learn,... on Amazon
Show steps
  • Read chapters 1-3 to obtain an overview of TensorFlow and its key concepts.
  • Summarize the main ideas and examples presented in the chapters.
Learn about ML Metadata with TFX
Gain more knowledge of ML Metadata, which is used extensively in the course.
Browse courses on ML Metadata
Show steps
  • Read the ML Metadata documentation
  • Complete the ML Metadata tutorial
Participate in a study group focused on ML pipelines
Enhance your understanding through collaborative learning, discussions, and problem-solving with peers.
Browse courses on ML Pipelines
Show steps
  • Join or form a study group with classmates.
  • Set regular meeting times to discuss course materials and work on projects together.
Learn about Cloud Composer for ML pipelines
Gain knowledge of Cloud Composer, a tool used in the course to orchestrate pipelines.
Browse courses on Cloud Composer
Show steps
  • Read the Cloud Composer documentation
  • Complete the Cloud Composer tutorial
Explore TensorFlow Extended Documentation
The TensorFlow Extended documentation provides comprehensive information on the platform's components and usage.
Browse courses on TensorFlow Extended
Show steps
  • Access the TensorFlow Extended documentation.
  • Review the introductory materials.
  • Explore specific topics relevant to your interests.
  • Utilize the search functionality to find specific information.
Follow tutorials on TensorFlow Extended (TFX)
Familiarize yourself with TFX, the platform used in the course, through practical examples.
Show steps
  • Explore the official TFX documentation and tutorials.
  • Implement a simple TFX pipeline using the provided examples.
Attend a workshop on ML pipeline automation
Expand your knowledge and skills by participating in a workshop tailored to ML pipeline automation, providing hands-on experience and exposure to industry best practices.
Show steps
  • Research and identify relevant ML pipeline automation workshops.
  • Attend the workshop and actively participate in the activities.
Attend meetups or conferences focused on machine learning pipelines
Engaging with the machine learning community through meetups or conferences will provide exposure to different perspectives and advancements in the field.
Browse courses on Machine Learning Pipelines
Show steps
  • Identify and attend relevant meetups or conferences
  • Connect with professionals and experts in the field
  • Share knowledge and learn from others
Practice problem solving with TFX
Engage in practice exercises and problem-solving scenarios to reinforce key TFX concepts and strengthen your pipeline building skills.
Show steps
  • Solve challenges involving TFX pipeline design
  • Troubleshoot common issues encountered during pipeline development
  • Optimize pipeline performance through practical exercises
Practice writing TFX pipeline components
Develop skills in writing TFX pipeline components, a fundamental element in the course.
Show steps
  • Create a simple TFX pipeline component
  • Use a TFX pipeline component in a pipeline
Build an end-to-end machine learning pipeline using TFX
Hands-on practice with TFX will enhance understanding of its capabilities and best practices in machine learning pipeline development.
Browse courses on TensorFlow Extended (TFX)
Show steps
  • Follow a comprehensive tutorial on TFX
  • Build a simple machine learning pipeline using TFX components
  • Deploy the pipeline and monitor its performance
Practice automating pipelines with CI/CD
Develop hands-on experience automating pipelines with CI/CD, an important topic in the course.
Browse courses on Pipeline Automation
Show steps
  • Set up a CI/CD pipeline for a TFX pipeline
  • Use a CI/CD pipeline to deploy a TFX pipeline
Solve coding challenges related to ML pipelines
Develop your coding skills specific to ML pipeline development and strengthen your understanding of the concepts covered in the course.
Browse courses on ML Pipelines
Show steps
  • Find coding challenges online or in textbooks related to ML pipelines.
  • Attempt to solve the challenges on your own.
  • Review your solutions and identify areas for improvement.
Explore advanced pipeline components
Expand your knowledge by following tutorials on building and integrating custom TFX components, deepening your understanding of pipeline orchestration.
Show steps
  • Seek out tutorials on creating custom TFX components
  • Follow tutorials on integrating custom components into TFX pipelines
  • Practice applying custom components to enhance pipeline functionality
Learn about MLflow for ML lifecycle management
Gain knowledge of MLflow, a tool used in the course to manage the ML lifecycle.
Browse courses on MlFlow
Show steps
  • Read the MLflow documentation
  • Complete the MLflow tutorial
Complete Practice Labs
Practice labs provide a hands-on environment to reinforce concepts covered in the course.
Browse courses on TensorFlow Extended
Show steps
  • Follow the instructions provided in the lab manuals.
  • Set up the necessary infrastructure and tools.
  • Execute the lab exercises.
  • Troubleshoot any errors encountered.
  • Review the results and identify areas for improvement.
Build a simple ML pipeline using TFX and Cloud Composer
Apply your knowledge by building a hands-on ML pipeline, solidifying your understanding of the concepts and gaining practical experience.
Browse courses on ML Pipelines
Show steps
  • Gather the necessary resources and set up your environment.
  • Design and implement an ML pipeline using TFX.
  • Integrate Cloud Composer for pipeline orchestration.
Solve coding challenges on machine learning pipeline orchestration
Solving coding challenges will reinforce understanding of the techniques and tools used in machine learning pipeline orchestration.
Browse courses on Cloud Composer
Show steps
  • Attempt coding challenges on platforms like LeetCode or HackerRank
  • Contribute to open-source projects involving machine learning pipeline orchestration
Create a blog post or presentation on ML pipeline best practices
Deepen your knowledge by researching and sharing best practices in ML pipeline development, enhancing your understanding and communication skills.
Browse courses on ML Pipelines
Show steps
  • Research and gather information on ML pipeline best practices.
  • Organize and structure your findings into a coherent presentation or blog post.
  • Share your work with others to receive feedback and engage in discussions.
Write a blog post or article on best practices in machine learning pipeline management
Creating content on machine learning pipeline management will solidify understanding and encourage critical thinking about best practices.
Show steps
  • Research and gather information on best practices in machine learning pipeline management
  • Write a draft of the blog post or article
  • Get feedback from peers or mentors
  • Publish the blog post or article
Build a Machine Learning Pipeline using TFX
Developing a real-world project allows you to apply and reinforce the concepts learned in the course.
Browse courses on TensorFlow Extended
Show steps
  • Identify a suitable use case for a machine learning pipeline.
  • Gather and prepare the necessary data.
  • Design and implement the pipeline using TFX.
  • Deploy and evaluate the pipeline.
Create a comprehensive study guide covering all course topics
Organizing and summarizing course materials will improve retention and provide a valuable resource for future reference.
Show steps
  • Review course materials, notes, and assignments
  • Identify and extract key concepts
  • Create a structured outline
  • Write and compile the study guide
Develop a TFX pipeline project
Apply your TFX pipeline skills by building a project that incorporates pipeline orchestration, handling ML metadata, and automating with CI/CD.
Browse courses on Machine Learning Pipelines
Show steps
  • Define the project scope and objectives
  • Design and implement the TFX pipeline
  • Automate the pipeline using CI/CD
  • Evaluate and refine the pipeline performance
Mentor junior machine learning engineers or students
Mentoring others will reinforce understanding of machine learning pipeline concepts and best practices.
Browse courses on Mentoring
Show steps
  • Identify opportunities to mentor others
  • Share knowledge and provide guidance
  • Receive feedback and learn from the mentee's perspective
Participate in Kaggle competitions
Put your ML pipeline skills to the test by participating in Kaggle competitions centered around TFX and pipeline topics.
Show steps
  • Identify relevant Kaggle competitions focused on TFX
  • Form a team or collaborate with others
  • Develop and implement your TFX-based pipeline solution
  • Submit your solution and track your progress
Participate in machine learning pipeline competitions
Participating in competitions will challenge understanding of machine learning pipelines and provide opportunities for practical application.
Show steps
  • Identify and register for relevant competitions
  • Build and optimize machine learning pipelines
  • Submit pipeline results and track performance

Career center

Learners who complete ML Pipelines on Google Cloud will develop knowledge and skills that may be useful to these careers:
Data Engineer
Data Engineers design, construct, manage, and maintain the data infrastructure that enables an organization to collect, process, and analyze data. Almost every industry employs Data Engineers, since every industry benefits from insights drawn from data. Many Data Engineers work with Machine Learning pipelines. This course will help you learn the fundamentals of designing and building ML pipelines.
See salaries and explore the career path for Data Engineer
Data Scientist
Data Scientists are responsible for collecting, cleaning, analyzing, and interpreting data to extract meaningful insights. Data Scientists analyze data using Machine Learning pipelines, which are automated processes that simplify and accelerate the development of ML models. This course will equip you with the skills to design and build ML pipelines for data analysis.
See salaries and explore the career path for Data Scientist
Machine Learning Engineer
Machine Learning Engineers develop, deploy, and maintain Machine Learning models. ML Engineers use Machine Learning pipelines to automate machine learning tasks, allowing them to be more efficient. ML Engineers collaborate with Data Scientists to build ML models. This course will help you build your skills in designing and building ML pipelines.
See salaries and explore the career path for Machine Learning Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. Some Software Engineers specialize in developing software for Machine Learning pipelines, which automate the development and deployment of ML models. This course can help Software Engineers build a foundation in ML pipelines.
See salaries and explore the career path for Software Engineer
Data Analyst
Data Analysts collect, clean, analyze, and interpret data to identify trends and patterns. Data Analysts may use Machine Learning pipelines to automate data analysis tasks, allowing them to be more efficient. This course can help Data Analysts build a foundation in ML pipelines.
See salaries and explore the career path for Data Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data and make predictions. Quantitative Analysts may employ Machine Learning pipelines to automate predictive modeling tasks, enhancing their efficiency. This course may be useful to Quantitative Analysts who want to learn more about ML pipelines.
See salaries and explore the career path for Quantitative Analyst
Business Analyst
Business Analysts analyze business processes to identify problems and inefficiencies. Business Analysts may use Machine Learning pipelines to help them automate data analysis tasks, allowing for greater efficiency. This course may be useful to Business Analysts who want to learn more about ML pipelines.
See salaries and explore the career path for Business Analyst
Statistician
Statisticians collect, analyze, interpret, and present data. Statisticians may use Machine Learning pipelines to automate statistical analysis tasks, enhancing their efficiency. This course may be useful to Statisticians who want to learn more about ML pipelines.
See salaries and explore the career path for Statistician
Operations Research Analyst
Operations Research Analysts use mathematical and analytical methods to solve business problems. Operations Research Analysts may employ Machine Learning pipelines to automate data analysis and optimization tasks, improving efficiency. This course may be useful to Operations Research Analysts who want to learn more about ML pipelines.
See salaries and explore the career path for Operations Research Analyst
Financial Analyst
Financial Analysts analyze financial data to make investment recommendations. Financial Analysts may use Machine Learning pipelines to automate data analysis tasks, allowing them to be more efficient. This course may be useful to Financial Analysts who want to learn more about ML pipelines.
See salaries and explore the career path for Financial Analyst
Market Researcher
Market Researchers collect and analyze data about markets and consumers. Market Researchers may use Machine Learning pipelines to automate data analysis tasks, enhancing their efficiency. This course may be useful to Market Researchers who want to learn more about ML pipelines.
See salaries and explore the career path for Market Researcher
Risk Analyst
Risk Analysts assess and mitigate risks. Risk Analysts may use Machine Learning pipelines to automate data analysis tasks, allowing them to be more efficient. This course may be useful to Risk Analysts who want to learn more about ML pipelines.
See salaries and explore the career path for Risk Analyst
Actuary
Actuaries use mathematical and statistical methods to assess risk and uncertainty. Actuaries may use Machine Learning pipelines to automate data analysis tasks, improving their efficiency. This course may be useful to Actuaries who want to learn more about ML pipelines.
See salaries and explore the career path for Actuary
Data Architect
Data Architects design and manage data systems. Data Architects may use Machine Learning pipelines to automate data management tasks, enhancing their efficiency. This course may be useful to Data Architects who want to learn more about ML pipelines.
See salaries and explore the career path for Data Architect
Database Administrator
Database Administrators manage and maintain databases. Database Administrators may use Machine Learning pipelines to automate database management tasks, improving their efficiency. This course may be useful to Database Administrators who want to learn more about ML pipelines.
See salaries and explore the career path for Database Administrator

Reading list

We've selected nine books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in ML Pipelines on Google Cloud.
Cover image
Fundamentals of Deep Learning
Save
Comprehensive introduction to TensorFlow, the open-source machine learning library. It covers all aspects of TensorFlow, from the basics to advanced topics such as deep learning.
Fundamentals of Deep Learning
Paperback
Check
Fundamentals of Deep Learning
Kindle Edition
Check
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
Hands-on guide to using Scikit-Learn, Keras, and TensorFlow for machine learning. It covers all aspects of machine learning, from data preprocessing to model evaluation.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Cover image
Deep Learning with Python, Second Edition
Save
Comprehensive introduction to deep learning, using Python and the Keras library. It covers all aspects of deep learning, from the basics to advanced topics such as natural language processing and computer vision.
Deep Learning with Python, Second Edition
Paperback
$$
Deep Learning with Python, Second Edition
Kindle Edition
$$
Machine Learning Design Patterns
Save
Provides a set of design patterns for machine learning. These patterns can help you to design and implement ML pipelines that are scalable, reliable, and maintainable.
Machine Learning Design Patterns
Paperback
Check
Machine Learning Design Patterns
Kindle Edition
Check
Cover image
Machine Learning in Action
Save
Provides a practical introduction to machine learning. It covers a wide range of machine learning algorithms, from simple to complex.
Machine Learning in Action
Paperback
$$
(中文) 机器学习实战 (图灵程序设计丛书 72) (Chinese Edition)
Kindle Edition
$
Cover image
The Elements of Statistical Learning
Save
Classic introduction to statistical learning. It covers a wide range of statistical learning methods, from linear regression to support vector machines.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Kindle Edition
$$$
Pattern Recognition and Machine Learning
Save
Provides a comprehensive introduction to pattern recognition and machine learning. It covers a wide range of topics, from the basics to advanced topics such as Bayesian methods and kernel methods.
Pattern Recognition and Machine Learning...
Hardcover
$$$
Pattern Recognition and Machine Learning...
Paperback
$$$
Pattern Recognition and Machine Learning (text...
Unknown Binding
$$$$
Cover image
Probabilistic Machine Learning
Save
Provides a comprehensive introduction to machine learning from a Bayesian and optimization perspective. It covers a wide range of topics, from the basics to advanced topics such as Gaussian processes and reinforcement learning.
Probabilistic Machine Learning: Advanced Topics...
Hardcover
$$$$
Cover image
Machine Learning with R
Save
Provides a step-by-step guide to using R for machine learning. It covers a wide range of machine learning algorithms, from simple to complex.
Machine Learning with R - Fourth Edition: Learn...
Paperback
$$
Machine Learning with R - Second Edition: Expert...
Kindle Edition
$$
Machine Learning with R - Third Edition: Expert...
Paperback
$$
(Italiano) Machine Learning con R: Conoscere le tecniche per...
Kindle Edition
$$
R: Unleash Machine Learning Techniques
Kindle Edition
$$$
Machine Learning with R: Learn How to Use R to...
Paperback
$$
(中文) Machine learning and R language (3rd edition of the...
Paperback
$$

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Course image
Time
185 hours
Level
Advanced
Via
Pluralsight
Instructor
Google Cloud
Language
English
Good to know
Taught by Google Cloud's ML engineers and trainers, indicating high-quality instruction
Covers the latest machine learning pipeline development techniques used at Google Cloud, providing industry-relevant knowledge
Provides hands-on, practical guidance on implementing and automating ML pipelines, enhancing learners' technical skills
Focuses on TensorFlow Extended, a vital tool for managing ML pipelines, giving learners specialized knowledge
Suitable for intermediate learners with some background in ML and pipeline orchestration, ensuring relevance to their learning journey
Requires learners to have a working knowledge of Cloud Composer and MLflow, which could be a barrier for those not familiar with these tools
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