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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

Chapter 12: DataRobot Python API

Users can access DataRobot's capabilities using DataRobot's Python client package. This lets us ingest data, create machine learning projects, make predictions from models, and manage models programmatically. It is easy to see the advantages that Application Programming Interfaces (APIs) offer users. The integrated use of Python and DataRobot lets us leverage the AutoML capabilities DataRobot presents, all while exploiting the programmatic flexibility and potential that Python possesses.

In this chapter, we will use the DataRobot Python API to ingest data, create a project with models, evaluate the models, and make predictions against them. At a high level, we will cover the following topics:

  • Accessing the DataRobot API
  • Understanding the DataRobot Python client
  • Building models programmatically
  • Making predictions programmatically
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