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

Data ingestion and data cataloging

Now that we have our datasets ready, we have two choices to bring them into DataRobot. We can go to either the Create New Project / Drag Dataset page (Figure 1.5) or the AI Catalog page (Figure 1.17). If the dataset is relatively small, we may prefer to start with the Create New Project method. After a few iterations, when the dataset has stabilized, you can move it into the AI Catalog page so that it can be reused in other projects.

Let's start by uploading our automobile dataset as a local file that we created in Chapter 4, Preparing Data for DataRobot. You can name the project Automobile Example 1, as shown in the following screenshot:

Figure 5.1 – Uploading dataset for a new project

You will notice that DataRobot automatically starts analyzing the data and performs a quick exploratory analysis. You can see that it found 30 features and 205 rows of data.

Note

If you are using an Excel file that has...