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 how to build and compare models by leveraging DataRobot's capabilities. As you saw, DataRobot makes it very easy to build many models quickly and helps us compare them. As you experienced, we tried many things and built dozens of models. This is DataRobot's key capability, and its importance to a data science team cannot be overstated. If you were to build these models on your own in Python, it would have taken a lot more time and effort. Instead, we used that time and thinking to experiment with different ideas and put more energy toward understanding the problem. We also learned about blueprints that encode best practices. These blueprints can be useful learning tools for new and experienced data scientists alike. We also learned how DataRobot can build ensemble or blended models for us.

It might be tempting to jump ahead and start deploying one of these models, but it is important to not directly jump to that without doing some analysis...