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

Future of DataRobot

DataRobot was the early pioneer in the AutoML space and seems to be the dominant player, but there are many others (H20, Kortical, and Google Cloud AutoML, to name a few) that are catching up rapidly. Many of the large cloud players are jumping into this space and have offerings that are very attractively priced. DataRobot continues to offer additional capabilities combined with good support from experienced data scientists. To that end, we expect that the DataRobot API will continue to evolve and become more robust to allow experienced data scientists to use DataRobot in a highly flexible and automated way.

We have noticed new capabilities being released even as this book is being written, such as the recent acquisition of the Zepl notebook platform. In addition to that, DataRobot continues to acquire other companies to round out its offerings. Recently, a lot of focus has been on MLOps and enabling the rapid deployment of models. As the features and capabilities...