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Agile Machine Learning with DataRobot

Agile Machine Learning with DataRobot

By : Bipin Chadha, Sylvester Juwe
4.8 (11)
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Agile Machine Learning with DataRobot

Agile Machine Learning with DataRobot

4.8 (11)
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)
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1
Section 1: Foundations
5
Section 2: Full ML Life Cycle with DataRobot: Concept to Value
11
Section 3: Advanced Topics

Chapter 6: Model Building with DataRobot

In this chapter, we will see how DataRobot is used to build models. Much of the model-building process has been automated, and DataRobot offers many capabilities to explore a wide range of algorithms automatically, as well as allowing data scientists to fine-tune what they want to build. This results in significant time savings for data science teams and leads to the exploration of many more models than would otherwise be possible. It also leads to better adherence to best practices and hence fewer chances of making mistakes.

By the end of this chapter, you will have learned how to utilize DataRobot to build a wide range of models. In this chapter, we're going to cover the following main topics:

  • Configuring a modeling project
  • Building models and the model leaderboard
  • Understanding model blueprints
  • Building ensemble models
CONTINUE READING
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Tech Concepts
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Programming languages
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Agile Machine Learning with DataRobot
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