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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

Building models programmatically

Now that we have imported the data, we will start building models programmatically. We will look at building the most basic models, then explore how to extract and visualize feature impact, before evaluating the performance of our models. Then, we will create more complex projects. Specifically, we will build one versus all multiclass classification models and model factories.

To create a DataRobot project, we must use the DataRobot Project.start method. The basic format for this is importing the necessary libraries (DataRobot, in the following case). Thereafter, the access credentials are presented, as described in the previous section. It is at the point that the Project method is called. project_name, sourcedata, and target are the minimal parameters that are required by the Project method for projects to be created. The project_name parameter tells DataRobot the name to give the created project. sourcedata provides information regarding the location...

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Agile Machine Learning with DataRobot
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