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Machine Learning Operations (MLOps) with Vertex AI
Manage Features
Google Cloud Training
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/pmx1um/machine
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
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
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
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Machine Learning Operations (MLOps) with Vertex AI: Manage Features.
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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
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.
Data Scientist
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.
Software Engineer
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.
DevOps 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.
Machine Learning 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.
Data Analyst
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.
Cloud Architect
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.
Data Engineer
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.
Product Manager
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.
Business Analyst
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.
Data Architect
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.
Project Manager
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.
Solutions Architect
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.
IT Manager
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.
Technical Architect
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.
For more career information including salaries, visit:
OpenCourser.com/course/pmx1um/machine
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.
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.
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.
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.
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.
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.
Provides practical examples of machine learning algorithms implemented in Python. It serves as a good resource for hands-on experience and reinforcement of concepts.
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.
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.
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.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/pmx1um/machine
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MLOps with Vertex AI: Manage Features - Français
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Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
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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