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Agile Machine Learning with DataRobot

Agile Machine Learning with DataRobot

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

Agile Machine Learning with DataRobot

4.8 (11)
By: Chadha, 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 8: Model Scoring and Deployment

In the previous chapter, we learned how to use outputs generated by DataRobot to understand models and why a model provides a particular prediction. We will now learn how to use models to score input datasets and create predictions to be used in the intended applications. DataRobot automates many tasks that are required for scoring and generating row-level explanations.

Creating predictions, however, is not where these tasks end. In most cases, these predictions need to be transformed into actions for consumption by people or applications. This mapping of predictions to actions requires an understanding of business and therefore needs a person to interpret the results (in most use cases). In this chapter, we will discuss how this is done. We're going to cover the following main topics:

  • Scoring and prediction methods
  • Generating prediction explanations
  • Analyzing predictions and postprocessing
  • Deploying DataRobot models...
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Agile Machine Learning with DataRobot
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