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

Summary

In this chapter, we covered some tools and methods to help you gain an understanding of your system and the business problem you are trying to solve. Some of these methods will be new or unfamiliar to even experienced data scientists, but it is important to take the time to internalize them and practice them on your projects. Some of this will feel unnecessary given the time pressures. This is one of the reasons tools such as DataRobot are beneficial, as they reduce the time you need to spend on repetitive tasks and allow you to focus on things that tools cannot do.

Hopefully, I have convinced you that the combination of data science teams focusing more on understanding the problem and using automation tools for some of the model building and tuning tasks provides the best value to an organization. A lot of the work done here will also come in handy toward the end of the project when we are getting ready to operationalize the models into the organization. Specifically, in...