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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

Accessing the DataRobot API

The programmatic use of DataRobot enables data experts to leverage the platform's efficacies while having the flexibility associated with typical programming. With the API access of DataRobot, data from numerous sources can be integrated for analytic or modeling purposes. This capability is not only limited to the data that's ingested, but also the output of the outcome. For instance, API access makes it possible for a customer risk profiling model to get data from differing sources, such as Google BigQuery, local files, as well as AWS S3 buckets. And in a few lines of codes, the outcomes can update records on Salesforce, as well as those surfaced on PowerBI via a BigQuery table. The strength of this multiple data source integration capability is furthered as this enables the automated, scheduled, end-to-end periodic refresh of model outcomes.

In this preceding case, it becomes possible for the client base to be rescored periodically. Regarding...

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