Book Image

Agile Machine Learning with DataRobot

By : Bipin Chadha, Sylvester Juwe
Book Image

Agile Machine Learning with DataRobot

By: Bipin Chadha, Sylvester Juwe

Overview of this book

DataRobot enables data science teams to become more efficient and productive. This book helps you to address machine learning (ML) challenges with DataRobot's enterprise platform, enabling you to extract business value from data and rapidly create commercial impact for your organization. You'll begin by learning how to use DataRobot's features to perform data prep and cleansing tasks automatically. The book then covers best practices for building and deploying ML models, along with challenges faced while scaling them to handle complex business problems. Moving on, you'll perform exploratory data analysis (EDA) tasks to prepare your data to build ML models and ways to interpret results. You'll also discover how to analyze the model's predictions and turn them into actionable insights for business users. Next, you'll create model documentation for internal as well as compliance purposes and learn how the model gets deployed as an API. In addition, you'll find out how to operationalize and monitor the model's performance. Finally, you'll work with examples on time series forecasting, NLP, image processing, MLOps, and more using advanced DataRobot capabilities. By the end of this book, you'll have learned to use DataRobot's AutoML and MLOps features to scale ML model building by avoiding repetitive tasks and common errors.
Table of Contents (19 chapters)
1
Section 1: Foundations
5
Section 2: Full ML Life Cycle with DataRobot: Concept to Value
11
Section 3: Advanced Topics

Technical requirements

Most parts of this chapter require access to the DataRobot software. The code example is based on a relatively small dataset, Book-Crossing, consisting of three tables, whose manipulation was carried out with Jupyter Notebook.

Check out the following video to see the Code in Action at https://bit.ly/3HxcNUL.

Book-Crossing dataset

The example used to illustrate the use of DataRobot in building recommendation systems is based on the Book-Crossing dataset by Cai-Nicolas Ziegler and colleagues. This dataset was accessed at http://www2.informatik.uni-freiburg.de/~cziegler/BX/.

Note

Before using this dataset, the authors of this book have informed the owner of the dataset about its use in this book.

Cai-Nicolas Ziegler, Sean M. McNee, Joseph A. Konstan, Georg Lausen (2005). Improving Recommendation Lists Through Topic Diversification. Proceedings of the 14th International World Wide Web Conference (WWW '05). May 10 – 14, 2005, Chiba...