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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 12: DataRobot Python API

Users can access DataRobot's capabilities using DataRobot's Python client package. This lets us ingest data, create machine learning projects, make predictions from models, and manage models programmatically. It is easy to see the advantages that Application Programming Interfaces (APIs) offer users. The integrated use of Python and DataRobot lets us leverage the AutoML capabilities DataRobot presents, all while exploiting the programmatic flexibility and potential that Python possesses.

In this chapter, we will use the DataRobot Python API to ingest data, create a project with models, evaluate the models, and make predictions against them. At a high level, we will cover the following topics:

  • Accessing the DataRobot API
  • Understanding the DataRobot Python client
  • Building models programmatically
  • Making predictions programmatically
CONTINUE READING
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Tech Concepts
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Programming languages
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