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

Chapter 7: Model Understanding and Explainability

In the last chapter, we learned how to build models, and we will now learn how to use output generated by DataRobot to understand the models and also use this information to explain why a model provides a particular prediction. As we have discussed before, this aspect is critically important to ensure that we are using the results correctly. DataRobot automates much of the task of creating charts and plots to help someone understand a model, but you still need to know how to interpret what it is showing in the context of the problem you are trying to solve. This is another reason why we will need people involved in the process, even if much of a task has been automated. As you can imagine, the task of interpreting the results will therefore become more and more valuable as the degree of automation increases.

In this chapter, we're going to cover the following main topics:

  • Reviewing and understanding model details
  • ...