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Machine Learning Operations (MLOps) with Vertex AI

Manage Features

Google Cloud Training

This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production.

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This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production.

Learners will get hands-on practice using Vertex AI Feature Store's streaming ingestion at the SDK layer.

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What's inside

Syllabus

Welcome to the Machine Learning Operations (MLOps) with Vertex AI: Manage Features
Introduction to the course.
Introduction to Vertex AI Feature Store
Read more
Vertex AI and its MLOps capabilities. Main challenges related to data and potential solutions to mitigate them.
Machine Learning Operations (MLOps) with Vertex AI: Manage Features An in depth look
Key capabilities of Vertex AI Feature Store
Summary
Summary of the course

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Develops skills in MLOps and streaming ingestion, which are critical for professionals dealing with machine learning operations
Taught by Google Cloud Training, an organization recognized for its expertise in cloud-based solutions and machine learning
Provides hands-on experience with Vertex AI Feature Store's streaming ingestion at the SDK layer, making the learning highly practical
Suited for individuals already familiar with machine learning concepts and operations, seeking to enhance their MLOps skills

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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 Machine Learning Operations (MLOps) with Vertex AI: Manage Features with these activities:
Sharpen your Python programming skills before the course starts
Ensure a solid foundation in Python to maximize your understanding of MLOps tools and techniques.
Browse courses on Python Programming
Show steps
  • Review Python basics and data structures.
  • Solve coding problems and practice writing Python code.
Engage in Study Sessions with Peers
Enhance your learning through collaborative discussions and knowledge sharing with fellow learners enrolled in the course.
Browse courses on Machine Learning
Show steps
  • Join a study group or create one with classmates.
  • Regularly meet to discuss course materials, share insights, and work through problems together.
  • Take turns presenting key concepts to strengthen understanding.
Explore Google Developers Training Platform Tutorials
Further your understanding of Vertex AI and associated ML tools by following tutorials on Google Developers Training Platform.
Browse courses on Vertex AI
Show steps
  • Visit Google Developers Training Platform and browse tutorials related to Vertex AI.
  • Complete at least one hands-on tutorial to gain practical experience.
  • Explore additional tutorials on specific topics that interest you.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Join a study group to discuss course concepts and share insights
Enhance your understanding through collaborative learning and peer support.
Browse courses on MLOps
Show steps
  • Find or create a study group with other students in the course.
  • Meet regularly to discuss assigned topics, share notes, and work through problems together.
Practice Vertex AI SDK with Code Exercises
Reinforce your understanding of Vertex AI functionalities by practicing hands-on coding exercises using its SDK.
Browse courses on Feature Store
Show steps
  • Set up a development environment and install the Vertex AI SDK.
  • Find coding exercises related to Vertex AI on platforms like LeetCode or HackerRank.
  • Practice solving these exercises to test your knowledge of Vertex AI APIs.
Deploy a simple ML model using Vertex AI
Build a strong foundation in deploying ML models on Google Cloud through hands-on practice.
Browse courses on Vertex AI
Show steps
  • Create a simple ML model using a pre-trained model or your own dataset.
  • Deploy the model to Vertex AI using the SDK.
  • Test the deployed model to ensure it meets your expectations.
Follow tutorials on Vertex AI documentation to explore advanced features
Expand your knowledge and skills by exploring advanced topics and features.
Browse courses on Vertex AI
Show steps
  • Identify areas where you want to enhance your knowledge.
  • Select relevant tutorials from the Vertex AI documentation.
  • Follow the tutorials step-by-step and implement the concepts in your own projects.
Experiment with Vertex AI Features on a Personal Project
Apply your knowledge of Vertex AI by experimenting with its features on a personal project.
Browse courses on Feature Store
Show steps
  • Ideate a project that aligns with your interests and leverages Vertex AI capabilities.
  • Gather and prepare a dataset.
  • Build and train an ML model using Vertex AI.
  • Evaluate the performance of your model and make necessary adjustments.
  • Write a blog or create a presentation to share your project and insights.
Build a full-fledged MLOps pipeline using Vertex AI
Gain practical experience in implementing an end-to-end MLOps workflow.
Browse courses on Vertex AI
Show steps
  • Define the problem statement and gather the necessary data.
  • Build a training pipeline using Vertex AI SDK.
  • Deploy the trained model to Vertex AI and integrate it with your application.
  • Monitor the model's performance and retrain it as needed.
Write a blog post summarizing the key takeaways from the course
Browse courses on MLOps
Show steps
  • Identify the most important concepts and techniques covered in the course.
  • Write a clear and concise summary of each concept, providing examples and explanations.
  • Share your blog post on social media or a relevant online forum.
Contribute to open-source projects related to MLOps
Gain practical experience and collaborate with the community to advance your MLOps knowledge.
Browse courses on MLOps
Show steps
  • Explore open-source repositories related to MLOps.
  • Identify areas where you can contribute your skills.
  • Fork the repository, make your changes, and submit a pull request.
Develop a Portfolio Project Utilizing Vertex AI Features
Solidify your learning by building a comprehensive project that showcases your skills in deploying, evaluating, and monitoring ML systems using Vertex AI.
Browse courses on Machine Learning
Show steps
  • Define the scope and goals of your project.
  • Gather and prepare a relevant dataset.
  • Design and implement an ML model using Vertex AI.
  • Evaluate the performance of your model.
  • Deploy and monitor your model in a production environment.

Career center

Learners who complete Machine Learning Operations (MLOps) with Vertex AI: Manage Features will develop knowledge and skills that may be useful to these careers:
Machine Learning Operations Engineer
This course is tailored for Machine Learning Operations Engineers who seek to enhance their skills in deploying, evaluating, monitoring, and operating ML systems on Google Cloud. With MLOps gaining prominence, this course will provide you with the knowledge and expertise to excel in this role. Vertex AI is the industry-leading platform for MLOps, and this course will equip you with the essential tools and best practices for its effective utilization.
See salaries and explore the career path for Machine Learning Operations Engineer
Data Scientist
This course is a great way to enter the field of Data Science or learn more about Machine Learning Operations (MLOps) if you are already a Data Scientist. Vertex AI is the top-of-the-line platform for MLOps, and this course covers the essential MLOps tools and best practices for deploying, evaluating, monitoring, and operating ML systems on Google Cloud. This course covers the fundamentals of MLOps with Vertex AI, which can enhance your ability to build and manage production ML systems.
See salaries and explore the career path for Data Scientist
Software Engineer
If you are a Software Engineer with an interest in MLOps, this course will introduce you to the essential tools and best practices for deploying, evaluating, monitoring, and operating ML systems on Google Cloud. Vertex AI is the top-of-the-line platform for MLOps, making it an excellent choice for building and managing production ML systems. This course may be particularly helpful if you are interested in working with Vertex AI Feature Store, which allows you to manage features for ML models and make them available to other teams.
See salaries and explore the career path for Software Engineer
DevOps Engineer
This course may be useful for DevOps Engineers who want to learn more about MLOps, the discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. As DevOps Engineers often work closely with ML teams, understanding MLOps can be valuable. This course will provide you with the essential tools and best practices for MLOps with Vertex AI, which is the leading platform for MLOps.
See salaries and explore the career path for DevOps Engineer
Machine Learning Engineer
This course may be helpful for Machine Learning Engineers who would like to learn more about deploying, evaluating, monitoring, and operating ML systems on Google Cloud. As an integral part of the ML lifecycle, MLOps requires effective management of features, and this course will help you get up to speed on how to do so with Vertex AI Feature Store.
See salaries and explore the career path for Machine Learning Engineer
Data Analyst
Data Analysts who are interested in expanding their knowledge of MLOps may find this course helpful. MLOps is the discipline focused on the deployment, testing, monitoring, and automation of ML systems in production, which is highly relevant to Data Analysts. This course will introduce you to the essential tools and best practices for MLOps with Vertex AI, helping you to build and manage production ML systems.
See salaries and explore the career path for Data Analyst
Cloud Architect
Cloud Architects who wish to enhance their knowledge of MLOps may benefit from this course. MLOps is a critical aspect of designing and deploying ML systems in the cloud, and this course will introduce you to the essential tools and best practices for MLOps with Vertex AI. Vertex AI is a fully managed MLOps platform that can help Cloud Architects build, deploy, and manage ML models at scale.
See salaries and explore the career path for Cloud Architect
Data Engineer
This course may be helpful for Data Engineers who are interested in learning more about MLOps. MLOps is the discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Data Engineers play a critical role in MLOps, as they are responsible for preparing and managing the data that is used to train and deploy ML models. This course will introduce you to the essential tools and best practices for MLOps with Vertex AI, which is the leading platform for MLOps.
See salaries and explore the career path for Data Engineer
Product Manager
Product Managers who work with ML-powered products may find this course helpful. MLOps is essential for deploying and managing ML systems in production, and this course will introduce you to the essential tools and best practices for MLOps with Vertex AI. Vertex AI is the leading platform for MLOps, and this course will help you to understand how to use it to build and manage successful ML-powered products.
See salaries and explore the career path for Product Manager
Business Analyst
Business Analysts who want to gain a better understanding of MLOps may benefit from this course. MLOps is the discipline focused on the deployment, testing, monitoring, and automation of ML systems in production, and it is becoming increasingly important for businesses to understand how to use ML effectively. This course will introduce you to the essential tools and best practices for MLOps with Vertex AI, which is the leading platform for MLOps.
See salaries and explore the career path for Business Analyst
Data Architect
Data Architects who are working with ML systems may find this course helpful. MLOps is the discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Data Architects play a critical role in MLOps, as they are responsible for designing and managing the data architecture of ML systems. This course will introduce you to the essential tools and best practices for MLOps with Vertex AI, which is the leading platform for MLOps.
See salaries and explore the career path for Data Architect
Project Manager
Project Managers who are working on ML projects may find this course helpful. MLOps is the discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Project Managers play a critical role in MLOps, as they are responsible for planning and executing the deployment of ML systems. This course will introduce you to the essential tools and best practices for MLOps with Vertex AI, which is the leading platform for MLOps.
See salaries and explore the career path for Project Manager
Solutions Architect
This course may be helpful for Solutions Architects who are working with ML systems. MLOps is the discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Solutions Architects play a critical role in MLOps, as they are responsible for designing and implementing solutions that meet the needs of customers. This course will introduce you to the essential tools and best practices for MLOps with Vertex AI, which is the leading platform for MLOps.
See salaries and explore the career path for Solutions Architect
IT Manager
IT Managers who are responsible for overseeing the deployment and management of ML systems may find this course helpful. MLOps is the discipline focused on the deployment, testing, monitoring, and automation of ML systems in production, and it is essential for ensuring that ML systems are deployed and managed effectively. This course will introduce you to the essential tools and best practices for MLOps with Vertex AI, which is the leading platform for MLOps.
See salaries and explore the career path for IT Manager
Technical Architect
Technical Architects who are designing and deploying ML systems may find this course helpful. MLOps is the discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Technical Architects play a critical role in MLOps, as they are responsible for designing the architecture of ML systems. This course will introduce you to the essential tools and best practices for MLOps with Vertex AI, which is the leading platform for MLOps.
See salaries and explore the career path for Technical Architect

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 Machine Learning Operations (MLOps) with Vertex AI: Manage Features.
Cover image
Feature Engineering for Machine Learning
Save
Provides a comprehensive overview of feature engineering principles and best practices, covering topics such as feature selection, transformation, and dimensionality reduction, and the resources that are available in Vertex AI. Useful as a background resource or a reference.
Feature Engineering for Machine Learning:...
Paperback
$$
Feature Engineering for Machine Learning:...
Kindle Edition
$$
Cover image
Designing Data-Intensive Applications
Save
Provides concepts and patterns for building fault-tolerant, scalable distributed systems for data-intensive processing. It is recommended as additional reading to gain a deeper understanding of system design for ML operations.
Designing Data-Intensive Applications: The Big...
Paperback
$$
Designing Data-Intensive Applications: The Big...
Kindle Edition
$$
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
TensorFlow widely used open-source library for machine learning. This practical guide provides hands-on experience in using TensorFlow for building, training, and deploying ML models. Valuable as a supplementary resource.
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
$$
Cover image
Fundamentals of Machine Learning for Predictive...
Save
Introduces the fundamental concepts and algorithms of machine learning, covering topics such as supervised learning, unsupervised learning, and ensemble methods. Useful for background knowledge or additional reading.
Fundamentals of Machine Learning for Predictive...
Hardcover
$$$
Fundamentals of Machine Learning for Predictive...
Kindle Edition
$$
Cover image
Introducing MLOps
Save
Introduces the concepts and practices of MLOps, providing a comprehensive overview of the field. Recommended as additional reading to gain a broader understanding of MLOps.
Introducing MLOps: How to Scale Machine Learning in...
Paperback
$$
Introducing MLOps: How to Scale Machine Learning in...
Kindle Edition
$$
PYTHON MACHINE LEARNING BY EXAMPLE
Save
Provides practical examples of machine learning algorithms implemented in Python. It serves as a good resource for hands-on experience and reinforcement of concepts.
Python Machine Learning By Example: Unlock machine...
Paperback
$$
Python Machine Learning by Example - Third Edition:...
Paperback
$$
Python Machine Learning By Example: The easiest way...
Paperback
$$
Python Machine Learning By Example - Second Edition...
Paperback
$$
Python Machine Learning By Example: Unlock machine...
Kindle Edition
$$
Python Machine Learning By Example: Build...
Kindle Edition
$$
Python Machine Learning By Example: Implement...
Kindle Edition
$$
Python Machine Learning By Example: The easiest way...
Kindle Edition
$$
Cover image
Probabilistic Machine Learning
Save
This advanced textbook provides a deep understanding of machine learning from a Bayesian perspective, covering topics such as probabilistic graphical models, inference, and optimization. Suitable as a reference for those interested in the theoretical underpinnings of ML.
Probabilistic Machine Learning: Advanced Topics...
Hardcover
$$$$
Interpretable Machine Learning
Save
Explores techniques for making machine learning models more interpretable, enabling users to understand and explain the predictions made by these models. Beneficial as a reference or for additional reading.
Interpretable Machine Learning: A Guide For Making...
Paperback
$$$
Cover image
Data Pipelines Pocket Reference
Save
This concise guide provides an overview of data pipelines and best practices for data engineers. Useful as a reference for understanding the broader context of ML operations.
Data Pipelines Pocket Reference
Paperback
Check
Data Pipelines Pocket Reference
Kindle Edition
Check

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MLOps with Vertex AI: Manage Features - Français
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Good to know
Develops skills in MLOps and streaming ingestion, which are critical for professionals dealing with machine learning operations
Taught by Google Cloud Training, an organization recognized for its expertise in cloud-based solutions and machine learning
Provides hands-on experience with Vertex AI Feature Store's streaming ingestion at the SDK layer, making the learning highly practical
Suited for individuals already familiar with machine learning concepts and operations, seeking to enhance their MLOps skills
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