Seattle Deep Learning

Want to discuss the contents during the reading week? Join the Slack Flyte MLOps Slack group and search for the "ai-book-club" channel. https://slack.flyte.org/

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About the book:
Title: Reliable Machine Learning
Authors: Cathy Chen, Niall Richard Murphy, Kranti Parisa, D. Sculley, Todd Underwood
Published: September 2022

Feel free to read at your own pace, but my goal will be these chapters per week:

Discussion Week 1:
1. Introduction
2. Data Management Principles
3. Basic Introduction To Models
4. Feature And Training Data

Discussion Week 2:
5. Evaluating Model Validity And Quality
6. Fairness, Privacy, And Ethical ML Systems
7. Training Systems
8. Serving

Discussion Week 3:
9. Monitoring And Observability For Models
10. Continuous ML
11. Incident Response
12. How Product And ML Interact

Discussion Week 4:
13. Integrating ML Into Your Organization
14. Practical ML Org Implementation Examples
15. Case Studies: MLOps In Practice

Book Description:
Whether you're part of a small startup or a multinational corporation, this practical book shows data scientists, software and site reliability engineers, product managers, and business owners how to run and establish ML reliably, effectively, and accountably within your organization. You'll gain insight into everything from how to do model monitoring in production to how to run a well-tuned model development team in a product organization.

By applying an SRE mindset to machine learning, authors and engineering professionals Cathy Chen, Kranti Parisa, Niall Richard Murphy, D. Sculley, Todd Underwood, and featured guest authors show you how to run an efficient and reliable ML system. Whether you want to increase revenue, optimize decision making, solve problems, or understand and influence customer behavior, you'll learn how to perform day-to-day ML tasks while keeping the bigger picture in mind.

You'll examine:

• What ML is: how it functions and what it relies on
• Conceptual frameworks for understanding how ML "loops" work
• How effective productionization can make your ML systems easily monitorable, deployable, and operable
• Why ML systems make production troubleshooting more difficult, and how to compensate accordingly
• How ML, product, and production teams can communicate effectively

Learn more about the book here: https://learning.oreilly.com/library/view/reliable-machine-learning/9781098106218/

Want to discuss the contents during the reading week? Join the Slack Flyte MLOps Slack group and search for the "ai-book-club" channel. https://slack.flyte.org/

-------------------------------------------------
About the book:
Title: Reliable Machine Learning
Authors: Cathy Chen, Niall Richard Murphy, Kranti Parisa, D. Sculley, Todd Underwood
Published: September 2022

Feel free to read at your own pace, but my goal will be these chapters per week:

Discussion Week 1:
1. Introduction
2. Data Management Principles
3. Basic Introduction To Models
4. Feature And Training Data

Discussion Week 2:
5. Evaluating Model Validity And Quality
6. Fairness, Privacy, And Ethical ML Systems
7. Training Systems
8. Serving

Discussion Week 3:
9. Monitoring And Observability For Models
10. Continuous ML
11. Incident Response
12. How Product And ML Interact

Discussion Week 4:
13. Integrating ML Into Your Organization
14. Practical ML Org Implementation Examples
15. Case Studies: MLOps In Practice

Book Description:
Whether you're part of a small startup or a multinational corporation, this practical book shows data scientists, software and site reliability engineers, product managers, and business owners how to run and establish ML reliably, effectively, and accountably within your organization. You'll gain insight into everything from how to do model monitoring in production to how to run a well-tuned model development team in a product organization.

By applying an SRE mindset to machine learning, authors and engineering professionals Cathy Chen, Kranti Parisa, Niall Richard Murphy, D. Sculley, Todd Underwood, and featured guest authors show you how to run an efficient and reliable ML system. Whether you want to increase revenue, optimize decision making, solve problems, or understand and influence customer behavior, you'll learn how to perform day-to-day ML tasks while keeping the bigger picture in mind.

You'll examine:

• What ML is: how it functions and what it relies on
• Conceptual frameworks for understanding how ML "loops" work
• How effective productionization can make your ML systems easily monitorable, deployable, and operable
• Why ML systems make production troubleshooting more difficult, and how to compensate accordingly
• How ML, product, and production teams can communicate effectively

Learn more about the book here: https://learning.oreilly.com/library/view/reliable-machine-learning/9781098106218/

Want to discuss the contents during the reading week? Join the Slack Flyte MLOps Slack group and search for the "ai-book-club" channel. https://slack.flyte.org/

-------------------------------------------------
About the book:
Title: Reliable Machine Learning
Authors: Cathy Chen, Niall Richard Murphy, Kranti Parisa, D. Sculley, Todd Underwood
Published: September 2022

Feel free to read at your own pace, but my goal will be these chapters per week:

Discussion Week 1:
1. Introduction
2. Data Management Principles
3. Basic Introduction To Models
4. Feature And Training Data

Discussion Week 2:
5. Evaluating Model Validity And Quality
6. Fairness, Privacy, And Ethical ML Systems
7. Training Systems
8. Serving

Discussion Week 3:
9. Monitoring And Observability For Models
10. Continuous ML
11. Incident Response
12. How Product And ML Interact

Discussion Week 4:
13. Integrating ML Into Your Organization
14. Practical ML Org Implementation Examples
15. Case Studies: MLOps In Practice

Book Description:
Whether you're part of a small startup or a multinational corporation, this practical book shows data scientists, software and site reliability engineers, product managers, and business owners how to run and establish ML reliably, effectively, and accountably within your organization. You'll gain insight into everything from how to do model monitoring in production to how to run a well-tuned model development team in a product organization.

By applying an SRE mindset to machine learning, authors and engineering professionals Cathy Chen, Kranti Parisa, Niall Richard Murphy, D. Sculley, Todd Underwood, and featured guest authors show you how to run an efficient and reliable ML system. Whether you want to increase revenue, optimize decision making, solve problems, or understand and influence customer behavior, you'll learn how to perform day-to-day ML tasks while keeping the bigger picture in mind.

You'll examine:

• What ML is: how it functions and what it relies on
• Conceptual frameworks for understanding how ML "loops" work
• How effective productionization can make your ML systems easily monitorable, deployable, and operable
• Why ML systems make production troubleshooting more difficult, and how to compensate accordingly
• How ML, product, and production teams can communicate effectively

Learn more about the book here: https://learning.oreilly.com/library/view/reliable-machine-learning/9781098106218/

Want to discuss the contents during the reading week? Join the Slack Flyte MLOps Slack group and search for the "ai-book-club" channel. https://slack.flyte.org/

-------------------------------------------------
About the book:
Title: Reliable Machine Learning
Authors: Cathy Chen, Niall Richard Murphy, Kranti Parisa, D. Sculley, Todd Underwood
Published: September 2022

Feel free to read at your own pace, but my goal will be these chapters per week:

Discussion Week 1:
1. Introduction
2. Data Management Principles
3. Basic Introduction To Models
4. Feature And Training Data

Discussion Week 2:
5. Evaluating Model Validity And Quality
6. Fairness, Privacy, And Ethical ML Systems
7. Training Systems
8. Serving

Discussion Week 3:
9. Monitoring And Observability For Models
10. Continuous ML
11. Incident Response
12. How Product And ML Interact

Discussion Week 4:
13. Integrating ML Into Your Organization
14. Practical ML Org Implementation Examples
15. Case Studies: MLOps In Practice

Book Description:
Whether you're part of a small startup or a multinational corporation, this practical book shows data scientists, software and site reliability engineers, product managers, and business owners how to run and establish ML reliably, effectively, and accountably within your organization. You'll gain insight into everything from how to do model monitoring in production to how to run a well-tuned model development team in a product organization.

By applying an SRE mindset to machine learning, authors and engineering professionals Cathy Chen, Kranti Parisa, Niall Richard Murphy, D. Sculley, Todd Underwood, and featured guest authors show you how to run an efficient and reliable ML system. Whether you want to increase revenue, optimize decision making, solve problems, or understand and influence customer behavior, you'll learn how to perform day-to-day ML tasks while keeping the bigger picture in mind.

You'll examine:

• What ML is: how it functions and what it relies on
• Conceptual frameworks for understanding how ML "loops" work
• How effective productionization can make your ML systems easily monitorable, deployable, and operable
• Why ML systems make production troubleshooting more difficult, and how to compensate accordingly
• How ML, product, and production teams can communicate effectively

Learn more about the book here: https://learning.oreilly.com/library/view/reliable-machine-learning/9781098106218/