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

Reviewing and understanding model details

In the last chapter, we created several models for different projects. DataRobot creates 10 to 20 models in a project, and it would be very onerous to look at and analyze the details of all of these models. You do not have to review each of these models, and it is common to review only the top few models before making a final selection. We will now look at the leaderboard for models in the Automobile Example 2 project and select the top model, as illustrated in the following screenshot:

Figure 7.1 – Model information

In the preceding screenshot, we selected the Model Info tab within the Describe tab to get a view of how large the model is and the expected time it takes to create predictions. This information is useful in real-time applications that are time-sensitive and need to score thousands of transactions quickly. Let's now go to the Feature Impact tab within the Understand tab, as shown in the following...