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This course equips machine learning practitioners with the essential tools, techniques, and best practices for evaluating both generative and predictive AI models. Model evaluation is a critical discipline for ensuring that ML systems deliver reliable, accurate, and high-performing results in production.
Read more
This course equips machine learning practitioners with the essential tools, techniques, and best practices for evaluating both generative and predictive AI models. Model evaluation is a critical discipline for ensuring that ML systems deliver reliable, accurate, and high-performing results in production.
Read more
This course equips machine learning practitioners with the essential tools, techniques, and best practices for evaluating both generative and predictive AI models. Model evaluation is a critical discipline for ensuring that ML systems deliver reliable, accurate, and high-performing results in production.
Participants will gain a deep understanding of various evaluation metrics, methodologies, and their appropriate application across different model types and tasks. The course will emphasize the unique challenges posed by generative AI models and provide strategies for tackling them effectively. By leveraging Google Cloud's Vertex AI platform, participants will learn how to implement robust evaluation processes for model selection, optimization, and continuous monitoring.
Register for this course and see more details by visiting:
OpenCourser.com/course/q05hyy/machine
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What's inside
Syllabus
Welcome to the Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation
This module covers the course objectives and provides an overview of the course structure.
Read more
Syllabus
Welcome to the Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation
This module covers the course objectives and provides an overview of the course structure.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Focuses on Vertex AI, which is a managed machine learning platform that accelerates AI adoption and reduces time to value, making it highly relevant for practitioners
Covers model evaluation, which is critical for ensuring that ML systems deliver reliable, accurate, and high-performing results in production environments
Presented by Google Cloud, which is recognized for its contributions to machine learning and its suite of cloud-based AI and machine learning products
Emphasizes the unique challenges posed by generative AI models and provides strategies for tackling them effectively, which is useful for staying current
Requires leveraging Google Cloud's Vertex AI platform, which may require a subscription or familiarity with the Google Cloud ecosystem
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Reviews summary
Practical vertex ai model evaluation
Learners say this course provides a highly relevant and practical introduction to model evaluation using Google Cloud's Vertex AI platform. Many found the modules covering key Vertex AI evaluation tools like Automatic Metrics and Side by Side evaluation to be particularly insightful and useful. The included hands-on labs and demonstrations are frequently highlighted as a strongest aspect, offering valuable experience with real-world application. While providing a solid foundation for MLOps evaluation practices, some reviewers noted it serves well as a starting point and could potentially explore certain advanced topics or lab troubleshooting in greater depth. Overall, it's seen as a valuable course for those focused on MLOps in a cloud environment.
Provides a strong base for MLOps evaluation practices.
"This course provides a solid foundation for anyone involved in MLOps model evaluation."
"It's a great starting point for understanding how to implement evaluation processes."
"I feel much more confident in approaching model evaluation tasks after taking this course."
"Gave me a good understanding of essential evaluation concepts."
Concepts are explained clearly and easy to follow.
"Explanations were clear and concise, making complex ideas understandable."
"The content explains evaluation metrics and their application very well."
"I appreciated how the course breaks down the unique challenges of generative AI evaluation."
"The instructors explain the concepts clearly."
Gain hands-on experience with Vertex AI evaluation tools.
"The labs were great, really helped me get hands-on with Vertex AI features."
"I found the sections on Vertex AI Automatic Metrics and Side by Side evaluation directly applicable to my work."
"Using Vertex AI for model evaluation in the exercises was very beneficial for practical understanding."
"The course gives you practical exposure to key evaluation services in Vertex AI."
Could benefit from more advanced topics or detail.
"Could use more in-depth coverage on advanced topics or troubleshooting specific lab issues."
"While excellent as an introduction, some parts could go deeper for experienced practitioners."
"I would have liked to see more complex scenarios covered in the labs."
"It serves well as an overview but might require supplementary learning for mastery."
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 with Vertex AI: Model Evaluation with these
activities:
Review Key Machine Learning Metrics
Show steps
Reinforce your understanding of fundamental machine learning evaluation metrics to better grasp the course's advanced evaluation techniques.
Browse courses on
Precision
Show steps
-
Review definitions and formulas for common classification and regression metrics.
-
Work through examples of calculating metrics from confusion matrices or prediction errors.
-
Identify the strengths and weaknesses of each metric in different scenarios.
Read 'Evaluating Machine Learning Models' by Alice Zheng
Show steps
Gain a deeper understanding of model evaluation principles and practices to enhance your ability to apply Vertex AI's evaluation tools effectively.
View
Evaluating Machine Learning Models
on Amazon
Show steps
-
Read the book, focusing on chapters related to metrics and validation.
-
Take notes on key concepts and techniques.
-
Relate the book's content to the course's modules on Vertex AI.
Explore Vertex AI Model Evaluation Tutorials
Show steps
Familiarize yourself with Vertex AI's model evaluation capabilities through hands-on tutorials to prepare for practical application in the course.
Show steps
-
Search for Vertex AI model evaluation tutorials on Google Cloud's documentation.
-
Follow a tutorial on evaluating a classification model using Vertex AI.
-
Follow a tutorial on evaluating a generative model using Vertex AI.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Read 'Machine Learning Engineering' by Andriy Burkov
Show steps
Gain a broader perspective on the MLOps lifecycle and how model evaluation fits into the overall process of building and deploying machine learning systems.
View
Machine Learning Engineering
on Amazon
Show steps
-
Read the book, focusing on sections related to model deployment and monitoring.
-
Take notes on key concepts and best practices.
-
Relate the book's content to the course's modules on Vertex AI and MLOps.
Evaluate a Pre-trained Model on Vertex AI
Show steps
Apply the course's teachings by evaluating a pre-trained model on Vertex AI, solidifying your understanding of evaluation metrics and methodologies.
Show steps
-
Choose a pre-trained model available on Vertex AI (e.g., image classification, text generation).
-
Prepare a dataset for evaluation.
-
Use Vertex AI's model evaluation tools to assess the model's performance.
-
Analyze the evaluation results and identify areas for improvement.
Blog Post: Generative AI Evaluation Challenges
Show steps
Deepen your understanding of generative AI evaluation by writing a blog post that summarizes the challenges and potential solutions discussed in the course.
Show steps
-
Research the unique challenges of evaluating generative AI models.
-
Outline the key points to cover in your blog post.
-
Write a clear and concise blog post explaining the challenges and potential solutions.
-
Publish your blog post on a platform like Medium or your personal website.
Contribute to an Open Source Evaluation Library
Show steps
Apply your knowledge and contribute to the broader machine learning community by contributing to an open-source model evaluation library.
Show steps
-
Identify an open-source model evaluation library on GitHub.
-
Review the library's documentation and contribution guidelines.
-
Identify a bug or feature request to work on.
-
Submit a pull request with your contribution.
Review Key Machine Learning Metrics
Show steps
Reinforce your understanding of fundamental machine learning evaluation metrics to better grasp the course's advanced evaluation techniques.
Browse courses on
Precision
Show steps
Show steps
Show steps
- Review definitions and formulas for common classification and regression metrics.
- Work through examples of calculating metrics from confusion matrices or prediction errors.
- Identify the strengths and weaknesses of each metric in different scenarios.
Read 'Evaluating Machine Learning Models' by Alice Zheng
Show steps
Gain a deeper understanding of model evaluation principles and practices to enhance your ability to apply Vertex AI's evaluation tools effectively.
View
Evaluating Machine Learning Models
on Amazon
Show steps
Show steps
Show steps
- Read the book, focusing on chapters related to metrics and validation.
- Take notes on key concepts and techniques.
- Relate the book's content to the course's modules on Vertex AI.
Explore Vertex AI Model Evaluation Tutorials
Show steps
Familiarize yourself with Vertex AI's model evaluation capabilities through hands-on tutorials to prepare for practical application in the course.
Show steps
Show steps
Show steps
- Search for Vertex AI model evaluation tutorials on Google Cloud's documentation.
- Follow a tutorial on evaluating a classification model using Vertex AI.
- Follow a tutorial on evaluating a generative model using Vertex AI.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Read 'Machine Learning Engineering' by Andriy Burkov
Show steps
Gain a broader perspective on the MLOps lifecycle and how model evaluation fits into the overall process of building and deploying machine learning systems.
View
Machine Learning Engineering
on Amazon
Show steps
Show steps
Show steps
- Read the book, focusing on sections related to model deployment and monitoring.
- Take notes on key concepts and best practices.
- Relate the book's content to the course's modules on Vertex AI and MLOps.
Evaluate a Pre-trained Model on Vertex AI
Show steps
Apply the course's teachings by evaluating a pre-trained model on Vertex AI, solidifying your understanding of evaluation metrics and methodologies.
Show steps
Show steps
Show steps
- Choose a pre-trained model available on Vertex AI (e.g., image classification, text generation).
- Prepare a dataset for evaluation.
- Use Vertex AI's model evaluation tools to assess the model's performance.
- Analyze the evaluation results and identify areas for improvement.
Blog Post: Generative AI Evaluation Challenges
Show steps
Deepen your understanding of generative AI evaluation by writing a blog post that summarizes the challenges and potential solutions discussed in the course.
Show steps
Show steps
Show steps
- Research the unique challenges of evaluating generative AI models.
- Outline the key points to cover in your blog post.
- Write a clear and concise blog post explaining the challenges and potential solutions.
- Publish your blog post on a platform like Medium or your personal website.
Contribute to an Open Source Evaluation Library
Show steps
Apply your knowledge and contribute to the broader machine learning community by contributing to an open-source model evaluation library.
Show steps
Show steps
Show steps
- Identify an open-source model evaluation library on GitHub.
- Review the library's documentation and contribution guidelines.
- Identify a bug or feature request to work on.
- Submit a pull request with your contribution.
Career center
Learners who complete Machine Learning Operations with Vertex AI: Model Evaluation will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A machine learning engineer focuses on building, deploying, and maintaining machine learning models. Model evaluation is a critical part of this work. This course directly addresses this aspect through its curriculum on best practices for evaluating both generative and predictive AI models. The course specifically helps a machine learning engineer understand evaluation metrics and methodologies, which is essential for model selection, optimization, and continuous monitoring. This course's emphasis on Vertex AI tools for robust evaluation is very useful for a machine learning engineer.
Artificial Intelligence Specialist
Artificial Intelligence Specialist
An artificial intelligence specialist designs and implements AI solutions, and a key aspect of their work is ensuring models function correctly. This course offers an excellent foundation for this, focusing on the specific techniques and best practices needed for evaluating both predictive and generative AI models. The course helps an artificial intelligence specialist learn about various evaluation metrics and methodologies, including handling the particular challenges of evaluating generative AI models. By using Google Cloud's Vertex AI platform for implementing evaluation processes, this course is very useful for an artificial intelligence specialist.
Data Scientist
Data Scientist
A data scientist uses statistical and machine learning techniques to gain insights from data. For data scientists, understanding model performance is key. This course provides the essential tools and knowledge to evaluate both generative and predictive AI models, with a focus on selecting the right metrics, methodologies, and their appropriate application across different model types and tasks. The course's curriculum helps a data scientist understand how to evaluate models effectively using Vertex AI.
AI Product Manager
AI Product Manager
An AI product manager is responsible for the strategy, roadmap, and execution of AI products. Having a solid grasp of model evaluation helps this role make informed decisions. This course helps the product manager by providing insights into evaluation metrics, methodologies, and their applications. A key aspect of this role is tackling the challenges of evaluating generative AI models, which this course addresses. This course also demonstrates how Vertex AI helps to implement robust evaluation processes.
AI Consultant
AI Consultant
An AI consultant provides expert advice to organizations on implementing AI solutions. For an AI consultant to be effective, they need a strong understanding of model evaluation. This course gives the consultant the tools and knowledge needed to evaluate both generative and predictive AI models. This course is also valuable to ensure that AI solutions are reliable and accurate. It also provides context around how to use Vertex AI to ensure models are selected, optimized, and continuously monitored.
Research Scientist
Research Scientist
A research scientist explores new scientific concepts within AI and machine learning, and validating their work requires advanced model evaluation. This course helps a research scientist by providing in-depth knowledge of evaluation metrics, methodologies, and their application. The course is useful because it focuses on the evaluation of generative AI models and also describes how to use Vertex AI for evaluation processes.
Machine Learning Educator
Machine Learning Educator
A machine learning educator teaches machine learning concepts to others. A deep understanding of model evaluation helps this role to communicate best practices and techniques. This course equips a machine learning educator with expert knowledge about metrics, methodologies, and their applications to both predictive and generative AI models. The course's focus on using Vertex AI makes it a good resource to share with students.
Data Analyst
Data Analyst
A data analyst examines data to draw conclusions and make recommendations. They may also perform model evaluation as part of this process. This course may help a data analyst to gain knowledge of model evaluation, metrics, and methodologies. The course describes the challenges of evaluating generative AI models, which may be useful to the data analyst. Familiarity with the Vertex AI platform could be beneficial to the data analyst.
Solutions Architect
Solutions Architect
A solutions architect designs and implements technology solutions for organizations, including those that use AI. An understanding of model evaluation is critical to the success of these implementations. This course may be useful to a solutions architect as it focuses on evaluation metrics, methodologies, and their application to different model types. The course also discusses how to implement robust evaluation processes using Google Cloud's Vertex AI platform, which could be beneficial for this role.
Software Engineer
Software Engineer
A software engineer develops and maintains software applications. There are times when their work involves incorporating machine learning models. This course may be useful to a software engineer by giving them a better grasp of model evaluation. The course focuses on the challenges of evaluating generative AI models and also showcases Vertex AI, which could be helpful in implementing machine learning models into software.
Business Intelligence Analyst
Business Intelligence Analyst
A business intelligence analyst analyzes data to provide business insights and may need to understand model evaluation if working with AI related data. This course may help a business intelligence analyst to understand model evaluation metrics and methodologies. The course's discussion on Vertex AI may be helpful to this role. Understanding the challenges of evaluating generative AI models could also be a benefit to the business intelligence analyst.
Technical Writer
Technical Writer
A technical writer creates documentation for technical products. The technical writer may need some understanding of evaluation metrics, methodologies, and their application. This course may be helpful to a technical writer by introducing them to the evaluation of predictive and generative AI models. This course may also be useful to a technical writer who needs to understand how to use the Vertex AI platform.
Project Manager
Project Manager
A project manager plans and oversees projects, including those that involve artificial intelligence. This course may help a project manager with the AI aspect of a project by understanding the importance of model evaluation. The course's overview of metrics, methodologies, and their application in the context of Vertex AI may be useful. An understanding of the challenges of evaluating generative AI may also provide context and enable them to manage related projects more effectively.
Quality Assurance Analyst
Quality Assurance Analyst
A quality assurance analyst tests systems and processes to ensure they meet standards. Some of these systems may involve AI. This course may help a quality assurance analyst to understand model evaluation process. The course's discussion of metrics, methodologies, and their application using Vertex AI may indirectly help the quality assurance analyst understand evaluation steps, which may be relevant to their work. The course may help understand the challenges of evaluating generative AI models.
Cloud Solutions Specialist
Cloud Solutions Specialist
A cloud solutions specialist helps organizations implement cloud based technologies, which may include AI solutions. While this course is not directly on cloud technologies, it features machine learning, which may be helpful to a cloud solutions specialist. This course shows how to use Vertex AI, a cloud platform, which may be useful. By understanding the evaluation of models, the cloud solutions specialist may better understand a client's needs regarding AI deployment.
For more career information including salaries, visit:
OpenCourser.com/course/q05hyy/machine
Reading list
We've selected two 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 with Vertex AI: Model Evaluation.
Provides a comprehensive overview of model evaluation techniques, covering both theoretical foundations and practical applications. It delves into various metrics, validation strategies, and error analysis methods. It is particularly useful for understanding the nuances of evaluating different types of models, including generative AI. This book serves as a valuable reference for practitioners seeking to improve their model evaluation skills.
Provides a broad overview of the machine learning engineering lifecycle, including model evaluation, deployment, and monitoring. While not solely focused on evaluation, it provides valuable context on how evaluation fits into the larger MLOps picture. It is particularly useful for understanding the practical considerations of deploying and maintaining models in production. This book is more valuable as additional reading to provide context.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/q05hyy/machine
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