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

Understanding model learning curves and trade-offs

In machine learning (ML) problems, we are always trying to find more data to improve our models, but as you can imagine, there comes a time when we reach a point of diminishing returns. It is very hard to know when you have reached that point, but you can get indications by looking at the learning curves. Fortunately, DataRobot makes that task easy by automatically building these learning curves. When DataRobot starts building models, it first tries a broad range of algorithms on small samples of data. Promising models are then built with bigger sample sizes, and so on.

In this process, we discover how much performance improvement happens as more data is added. To look at the learning curves, you can click on the Learning Curves menu item at the top of the screen, as seen in the following screenshot:

Figure 7.21 – Model learning curves

You can see the different model types on the right-hand side of...