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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
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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

Future of automated machine learning

It has been over 5 years since automated ML tools started appearing in the data science community. There are now several open source tools (for example, TPOT and Auto-WEKA) as well as proprietary tools (for example, Kortical and H2O) on the market. All major cloud providers now have some AutoML offering. Interest in these tools has been rising. It is safe to say that interest in these tools will keep rising and more offerings should be expected to come onto the market. We expect the tools to keep expanding the scope of tasks that will be automated to cover more aspects of the model development process. We can already see tools such as DataRobot offering more functions through internal development as well as through acquisitions such as Paxata. While it remains true that these tools do not support all use cases or types of modeling, they do cover a large number of use cases in a typical organization. More capabilities and algorithms are being added...

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