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 2: Machine Learning Basics

This chapter covers some basic concepts of machine learning that will be used and referenced in this book. This is the bare minimum you need to know in order to use DataRobot effectively. Experienced data scientists can safely skip this chapter. It is not the intention of this chapter to give you a comprehensive understanding of statistics or machine learning, but just a refresher of some key ideas and concepts. Also, the focus is on practical aspects of what you need to know in order to understand the core ideas without going into too much detail. It might be tempting to jump in and let DataRobot automatically build the models, but doing that without a basic understanding could backfire. If you are leading a data science team, please make sure that you have experienced data scientists in your teams who are mentoring others and that there are other governance processes in place.

Some of these concepts will come up again during the hands-on examples...