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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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Toc

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

Summary

In this chapter, we have extensively examined how DataRobot could be used to build time series models. We briefly discussed the unique opportunities time series modeling presents businesses, as well as the challenges it presents for analysts and data scientists. We used DataRobot to create both single and multiple time series models. We also described how predictions could be made using models built by DataRobot. This was followed by a discussion on advanced aspects of DataRobot's time series capabilities.

Forecasting is extremely important to business because of its ability to foretell what is likely to occur in the future considering time-dependent variables. Another commercially valuable area is the ability to suggest the interest that differing clients would have for a wide array of products. This is where recommender systems come in.

In the next chapter, Chapter 10, Recommender Systems, we look at how DataRobot could be used to build recommender engines.

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