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 have extensively examined how DataRobot could be used to build time series models. We briefly discussed the unique opportunities time series modeling presents businesses, as well as the challenges it presents for analysts and data scientists. We used DataRobot to create both single and multiple time series models. We also described how predictions could be made using models built by DataRobot. This was followed by a discussion on advanced aspects of DataRobot's time series capabilities.

Forecasting is extremely important to business because of its ability to foretell what is likely to occur in the future considering time-dependent variables. Another commercially valuable area is the ability to suggest the interest that differing clients would have for a wide array of products. This is where recommender systems come in.

In the next chapter, Chapter 10, Recommender Systems, we look at how DataRobot could be used to build recommender engines.

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