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 quality assessment

DataRobot will also perform a data quality assessment and notify you if it finds any data issues, as shown in the following screenshot:

Figure 5.2 – Data quality issues

In this case, it has found outliers in eight features. You can look into the details to see if these look acceptable or if you need to drop or otherwise fix these outliers. We will do this as we explore and analyze each of these features in the following section.

Notice that it also looked for any disguised missing values or excess zeros in any feature. These can be hard to detect manually and can be problematic for your models, so it is important to fix these issues if they come up. For example, you saw in Chapter 4, Preparing Data for DataRobot, that we already fixed the issue of excess zeros in the normalized-losses feature. If we had not done that previously, DataRobot would alert us to fix this or filter out those rows before proceeding. It will also perform...