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Agile Machine Learning with DataRobot

You're reading from   Agile Machine Learning with DataRobot Automate each step of the machine learning life cycle, from understanding problems to delivering value

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Product type Paperback
Published in Dec 2021
Publisher Packt
ISBN-13 9781801076807
Length 344 pages
Edition 1st Edition
Languages
Concepts
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Authors (2):
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Bipin Chadha Bipin Chadha
Author Profile Icon Bipin Chadha
Bipin Chadha
Sylvester Juwe Sylvester Juwe
Author Profile Icon Sylvester Juwe
Sylvester Juwe
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Foundations
2. Chapter 1: What Is DataRobot and Why You Need It? FREE CHAPTER 3. Chapter 2: Machine Learning Basics 4. Chapter 3: Understanding and Defining Business Problems 5. Section 2: Full ML Life Cycle with DataRobot: Concept to Value
6. Chapter 4: Preparing Data for DataRobot 7. Chapter 5: Exploratory Data Analysis with DataRobot 8. Chapter 6: Model Building with DataRobot 9. Chapter 7: Model Understanding and Explainability 10. Chapter 8: Model Scoring and Deployment 11. Section 3: Advanced Topics
12. Chapter 9: Forecasting and Time Series Modeling 13. Chapter 10: Recommender Systems 14. Chapter 11: Working with Geospatial Data, NLP, and Image Processing 15. Chapter 12: DataRobot Python API 16. Chapter 13: Model Governance and MLOps 17. Chapter 14: Conclusion 18. Other Books You May Enjoy

Determining predictions, actions, and consequences for Responsible AI

After the model is built and deployed in DataRobot, it might seem that our job is done—but not so fast. You should start analyzing what the predictions profile looks like and start discussing with users and stakeholders the details of actions to be taken. The models you have helped build are likely to introduce many changes in your system and will impact other people. It is therefore important to try to make sure that these impacts are not negative. Making sure that your models will not cause harm is called Responsible AI. This will build upon the work you did during the understanding phase through various diagrams.

Just as in previous sections we saw how a causal diagram helps you to relate features to a target, we can also see how the target affects other parts of the system. The diagram should reveal how the target impacts key objectives or outcomes; it should also reveal key feedback loops that will...

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