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Kirsten Gokay,
Meeta Dash,
Alyssa Simpson-Rochwerger,
Andrea Butkovic,
and
Kiran Vajapey
As a product manager, you should be constantly looking to improve your machine learning models and product; learn strategies to mitigate bias, scale a product, and continuously update a model.
Register for this course and see more details by visiting:
OpenCourser.com/course/d2h45v/measuring
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What's inside
Syllabus
Introduction to the Measuring Impact and Updating Models course.
Learn best practices for measuring model success, strategies for mitigating unwanted bias in a model, and scaling an AI product so that it's available to a large audience.
Read more
Syllabus
Introduction to the Measuring Impact and Updating Models course.
Learn best practices for measuring model success, strategies for mitigating unwanted bias in a model, and scaling an AI product so that it's available to a large audience.
Read more
Review an end-to-end, AI product development cycle from solution ideation to prototyping and testing and finally, product launch (and continuous improvement) for a video annotation product.
Complete a proposal for a complete AI product of your own design; consider users, data source, design practices, and iteration over time.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops strategies to mitigate bias, which is a common challenge in AI development
Examines the end-to-end AI product development cycle, from ideation to launch
Provides real-world examples of AI product development, making the learning experience more practical
Taught by experienced instructors with expertise in product management and AI
Requires foundational knowledge in product management and AI, making it suitable for intermediate learners
Save this course
Save Measuring Impact and Updating Models to your list so you can find it easily later:
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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 Measuring Impact and Updating Models with these
activities:
Review Linear Algebra Basics
Show steps
Review basic linear algebra concepts before taking this course, which requires a solid understanding of linear algebra.
Browse courses on
Vectors
Show steps
-
Review the concepts of vectors, matrices, and linear transformations.
-
Solve practice problems involving linear algebra operations.
-
Go through online tutorials or textbooks to refresh your knowledge.
Participate in peer discussion groups on model updating
Show steps
Engage with peers to exchange ideas and knowledge on model updating.
Browse courses on
Machine Learning Models
Show steps
-
Join a peer discussion group.
-
Actively participate in discussions on model updating techniques.
-
Share your own experiences and insights.
Participate in a Study Group on Model Continuous Improvement
Show steps
Engage with peers to discuss strategies for continuous improvement of machine learning models.
Browse courses on
Continuous Improvement
Show steps
-
Form a study group with classmates or fellow practitioners.
-
Establish regular meeting times and a communication channel.
-
Choose specific topics related to model continuous improvement for discussion.
-
Share knowledge, experiences, and best practices within the group.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Complete practice problems on bias mitigation techniques
Show steps
Reinforce your understanding of bias mitigation strategies by completing practice problems.
Browse courses on
Bias Mitigation
Show steps
-
Identify common types of bias in machine learning models.
-
Explore techniques for mitigating bias, such as data sampling and algorithmic fairness.
-
Apply these techniques to real-world scenarios.
Explore Case Studies on Machine Learning Model Bias
Show steps
Supplement your learning by exploring real-world examples of bias in machine learning models.
Browse courses on
Bias in Machine Learning
Show steps
-
Identify reputable sources for case studies on machine learning model bias.
-
Analyze the case studies to understand the causes and consequences of bias.
-
Discuss the findings with peers or mentors to gain diverse perspectives.
-
Reflect on the implications for your own machine learning projects.
Solve Practice Problems on Model Evaluation Metrics
Show steps
Reinforce your understanding of model evaluation metrics through practice problems.
Browse courses on
Model Evaluation
Show steps
-
Gather a collection of practice problems on model evaluation metrics.
-
Solve the problems, focusing on accuracy, precision, recall, and other relevant metrics.
-
Compare your solutions with provided answers or consult with peers for feedback.
Create a case study on scaling an AI product
Show steps
Deepen your understanding of AI product scaling by creating a case study.
Show steps
-
Select an AI product that you are familiar with.
-
Identify the challenges associated with scaling the product.
-
Research and propose solutions to these challenges.
-
Present your case study to the class.
Develop a prototype of an AI product from scratch
Show steps
Apply your learning by developing a complete AI product.
Show steps
-
Ideate and define the problem you want to solve.
-
Design and develop the AI model.
-
Build the user interface and integrate the AI model.
-
Test and evaluate the product.
-
Iterate and improve the product based on feedback.
Mentor Junior Machine Learning Practitioners
Show steps
Consolidate your knowledge and aid others by mentoring junior machine learning practitioners.
Show steps
-
Identify opportunities to mentor junior practitioners, such as through online forums or local meetups.
-
Share your knowledge and experience, providing guidance and support.
-
Help mentees develop their skills and confidence in machine learning.
-
Receive feedback from mentees to enhance your own understanding.
Design a Machine Learning Model Scaling Plan
Show steps
Apply your knowledge by creating a comprehensive plan for scaling a machine learning model.
Browse courses on
Model Deployment
Show steps
-
Determine the target audience and use case for your machine learning model.
-
Research and evaluate different cloud computing platforms for model deployment.
-
Design a scalable architecture for your model, considering factors such as data volume, model complexity, and latency requirements.
-
Develop a testing and monitoring strategy to ensure the model's performance and availability.
-
Document your plan and share it with stakeholders for feedback and validation.
Contribute to Open-Source Machine Learning Projects
Show steps
Enhance your practical skills and knowledge by contributing to open-source machine learning projects.
Browse courses on
Community Involvement
Show steps
-
Identify reputable open-source machine learning projects on platforms like GitHub.
-
Review the project documentation and codebase.
-
Identify areas where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
-
Submit pull requests with your contributions and engage with the project maintainers.
Review Linear Algebra Basics
Show steps
Review basic linear algebra concepts before taking this course, which requires a solid understanding of linear algebra.
Browse courses on
Vectors
Show steps
Show steps
Show steps
- Review the concepts of vectors, matrices, and linear transformations.
- Solve practice problems involving linear algebra operations.
- Go through online tutorials or textbooks to refresh your knowledge.
Participate in peer discussion groups on model updating
Show steps
Engage with peers to exchange ideas and knowledge on model updating.
Browse courses on
Machine Learning Models
Show steps
Show steps
Show steps
- Join a peer discussion group.
- Actively participate in discussions on model updating techniques.
- Share your own experiences and insights.
Participate in a Study Group on Model Continuous Improvement
Show steps
Engage with peers to discuss strategies for continuous improvement of machine learning models.
Browse courses on
Continuous Improvement
Show steps
Show steps
Show steps
- Form a study group with classmates or fellow practitioners.
- Establish regular meeting times and a communication channel.
- Choose specific topics related to model continuous improvement for discussion.
- Share knowledge, experiences, and best practices within the group.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Complete practice problems on bias mitigation techniques
Show steps
Reinforce your understanding of bias mitigation strategies by completing practice problems.
Browse courses on
Bias Mitigation
Show steps
Show steps
Show steps
- Identify common types of bias in machine learning models.
- Explore techniques for mitigating bias, such as data sampling and algorithmic fairness.
- Apply these techniques to real-world scenarios.
Explore Case Studies on Machine Learning Model Bias
Show steps
Supplement your learning by exploring real-world examples of bias in machine learning models.
Browse courses on
Bias in Machine Learning
Show steps
Show steps
Show steps
- Identify reputable sources for case studies on machine learning model bias.
- Analyze the case studies to understand the causes and consequences of bias.
- Discuss the findings with peers or mentors to gain diverse perspectives.
- Reflect on the implications for your own machine learning projects.
Solve Practice Problems on Model Evaluation Metrics
Show steps
Reinforce your understanding of model evaluation metrics through practice problems.
Browse courses on
Model Evaluation
Show steps
Show steps
Show steps
- Gather a collection of practice problems on model evaluation metrics.
- Solve the problems, focusing on accuracy, precision, recall, and other relevant metrics.
- Compare your solutions with provided answers or consult with peers for feedback.
Create a case study on scaling an AI product
Show steps
Deepen your understanding of AI product scaling by creating a case study.
Show steps
Show steps
Show steps
- Select an AI product that you are familiar with.
- Identify the challenges associated with scaling the product.
- Research and propose solutions to these challenges.
- Present your case study to the class.
Develop a prototype of an AI product from scratch
Show steps
Apply your learning by developing a complete AI product.
Show steps
Show steps
Show steps
- Ideate and define the problem you want to solve.
- Design and develop the AI model.
- Build the user interface and integrate the AI model.
- Test and evaluate the product.
- Iterate and improve the product based on feedback.
Mentor Junior Machine Learning Practitioners
Show steps
Consolidate your knowledge and aid others by mentoring junior machine learning practitioners.
Show steps
Show steps
Show steps
- Identify opportunities to mentor junior practitioners, such as through online forums or local meetups.
- Share your knowledge and experience, providing guidance and support.
- Help mentees develop their skills and confidence in machine learning.
- Receive feedback from mentees to enhance your own understanding.
Design a Machine Learning Model Scaling Plan
Show steps
Apply your knowledge by creating a comprehensive plan for scaling a machine learning model.
Browse courses on
Model Deployment
Show steps
Show steps
Show steps
- Determine the target audience and use case for your machine learning model.
- Research and evaluate different cloud computing platforms for model deployment.
- Design a scalable architecture for your model, considering factors such as data volume, model complexity, and latency requirements.
- Develop a testing and monitoring strategy to ensure the model's performance and availability.
- Document your plan and share it with stakeholders for feedback and validation.
Contribute to Open-Source Machine Learning Projects
Show steps
Enhance your practical skills and knowledge by contributing to open-source machine learning projects.
Browse courses on
Community Involvement
Show steps
Show steps
Show steps
- Identify reputable open-source machine learning projects on platforms like GitHub.
- Review the project documentation and codebase.
- Identify areas where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
- Submit pull requests with your contributions and engage with the project maintainers.
Career center
Learners who complete Measuring Impact and Updating Models will develop knowledge and skills
that may be useful to these careers:
Product Manager
Product Manager
As a Product Manager, you will be responsible for the development and launch of new products or features. This course may be useful for you because it will teach you about measuring model success, mitigating unwanted bias in a model, and scaling an AI product. These are all skills that are essential for a Product Manager to have in order to be successful at launching new products or features.
Machine Learning Engineer
Machine Learning Engineer
As a Machine Learning Engineer, you will design, build, and maintain machine learning models. This course may be useful for you because it will teach you about measuring model success, mitigating unwanted bias in a model, and scaling an AI product. These are all skills that are essential for a Machine Learning Engineer to have in order to be successful.
Data Scientist
Data Scientist
As a Data Scientist, you will use data to solve business problems. This course may be useful for you because it will teach you about measuring model success, mitigating unwanted bias in a model, and scaling an AI product. These are all skills that are essential for a Data Scientist to have in order to be successful at using data to solve business problems.
Data Analyst
Data Analyst
As a Data Analyst, you will use data to identify trends and patterns. This course may be useful for you because it will teach you about measuring model success and mitigating unwanted bias in a model, which are both important skills for a Data Analyst to have.
Quantitative Analyst
Quantitative Analyst
As a Quantitative Analyst, you will use mathematics and statistics to solve financial problems. This course may be useful for you because it will teach you about measuring model success and mitigating unwanted bias in a model, which are both important skills for a Quantitative Analyst to have.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
As an AI Engineer, you will make AI models that are used for products or services. This course may be useful for you because it will teach you about measuring model success and mitigating unwanted bias in a model, which are important skills for an AI Engineer to have. Additionally, this course will help you to learn about scaling an AI product so that it's available to a large audience, which is a valuable skill for anyone working in the field of AI.
Consultant
Consultant
As a Consultant, you will advise clients on how to solve business problems. This course may be useful for you because it will teach you about measuring model success and mitigating unwanted bias in a model, which are both important skills for a Consultant to have.
User Experience Researcher
User Experience Researcher
As a User Experience Researcher, you will study user behavior to improve the user experience of products and services. This course may be useful for you because it will teach you about measuring model success and mitigating unwanted bias in a model, which are both important skills for a User Experience Researcher to have.
Market Research Analyst
Market Research Analyst
As a Market Research Analyst, you will use data to understand customer needs and preferences. This course may be useful for you because it will teach you about measuring model success and mitigating unwanted bias in a model, which are both important skills for a Market Research Analyst to have.
Product Designer
Product Designer
As a Product Designer, you will design and develop new products and services. This course may be useful for you because it will teach you about measuring model success and mitigating unwanted bias in a model, which are both important skills for a Product Designer to have.
Software Engineer
Software Engineer
As a Software Engineer, you will design, build, and maintain software applications. This course may be useful for you because it will teach you about the machine learning lifecycle, which is a valuable skill for any Software Engineer to have. Additionally, this course will help you to learn about scaling an AI product so that it's available to a large audience, which is a valuable skill for anyone working in the field of software engineering.
Business Analyst
Business Analyst
As a Business Analyst, you will use data to identify business problems and opportunities. This course may be useful for you because it will teach you about measuring model success and mitigating unwanted bias in a model, which are both important skills for a Business Analyst to have.
Entrepreneur
Entrepreneur
As an Entrepreneur, you will start and run your own business. This course may be useful for you because it will teach you about the machine learning lifecycle, which is valuable for any Entrepreneur to know. Additionally, this course will help you to learn about scaling an AI product so that it's available to a large audience, which is a valuable skill for any Entrepreneur.
Teacher
Teacher
As a Teacher, you will educate students about a particular subject. This course may be useful for you because it will teach you about the machine learning lifecycle, which is valuable for any Teacher to know. Additionally, this course will help you to learn about scaling an AI product so that it's available to a large audience, which is a valuable skill for any Teacher.
Writer
Writer
As a Writer, you will create written content for a variety of purposes. This course may be useful for you because it will teach you about the machine learning lifecycle, which is valuable for any Writer to know. Additionally, this course will help you to learn about scaling an AI product so that it's available to a large audience, which is a valuable skill for any Writer.
For more career information including salaries, visit:
OpenCourser.com/course/d2h45v/measuring
Reading list
We've selected 11 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
Measuring Impact and Updating Models.
Provides an extensive hands-on guide to building and evaluating machine learning models using popular Python libraries.
Save
Offers a comprehensive overview of deep learning, covering both theoretical foundations and practical applications.
Provides a probabilistic perspective on machine learning, helping learners understand the underlying mathematical foundations.
Offers a balanced introduction to both supervised and unsupervised machine learning techniques.
Provides a comprehensive overview of statistical pattern recognition and machine learning, with a focus on Bayesian methods.
Offers a practical guide to using machine learning tools and techniques for solving real-world problems.
Offers a practical guide to building and deploying machine learning models using Python.
Provides a hands-on introduction to data science, covering data cleaning, analysis, and visualization.
Offers a practical guide to building and evaluating machine learning models using R.
Provides a comprehensive guide to building and deploying machine learning models using Python.
Provides a practical guide to building and deploying machine learning models using Go.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/d2h45v/measuring
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Time
30240 hours
Level
Introductory
Via
Udacity
Instructors
Kirsten Gokay
Meeta Dash
Alyssa Simpson-Rochwerger
Andrea Butkovic
Kiran Vajapey
Language
English
Good to know
Develops strategies to mitigate bias, which is a common challenge in AI development
Examines the end-to-end AI product development cycle, from ideation to launch
Provides real-world examples of AI product development, making the learning experience more practical
Taught by experienced instructors with expertise in product management and AI
Requires foundational knowledge in product management and AI, making it suitable for intermediate learners
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