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 example utilizes a relatively small dataset, Book-Crossing, consisting of three tables, whose manipulation was described earlier in Chapter 10, Recommender Systems. As will be covered in the data description, we will create new fields in addition to those used in Chapter 10, Recommender Systems.

Book-Crossing dataset

The example used to illustrate the aspects of model governance is the same as the one used for building recommendation systems in Chapter 10, Recommender Systems. The dataset is based on the Book-Crossing dataset by Cai-Nicolas Ziegler and colleagues (http://www2.informatik.uni-freiburg.de/~cziegler/BX/). The data was collected during a 4-week crawl from the Book-Crossing community between August and September 2004.

Important Note

Before using this dataset, the authors of this book have informed the owner of the dataset about its usage in this book:

Cai-Nicolas...