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

Configuring a modeling project

In the previous chapter, we created a project and performed data analysis. We also saw that DataRobot automatically built several models for us. To build these models, we used default project settings.

In this section, we will cover what DataRobot did for us by default and look at how we can fine-tune that behavior. If you remember, once we click the Start button on the project page (see Figure 5.1 in Chapter 5, Exploratory Data Analysis with DataRobot), we cannot make any changes to the project options. We will therefore create a new project to review and select the options we want.

For this, let's go into DataRobot and select the Create New Project menu option. Just as before, we will now upload the same automobile dataset file that we used before. This time, you can name the project Automobile Example 2, as illustrated in the following screenshot:

Figure 6.1 – Uploading the dataset for a new project

You can...