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

Understanding model blueprints

DataRobot performs a lot of data transformations and hyperparameter tuning while building a model. It leverages a lot of best practices to build a specific type of model, and these best practices are codified in the form of blueprints. You can inspect these blueprints to gain insights into these best practices and also to better understand which steps were taken to build a model. To inspect the blueprint for a model, you can click on a model, go to the Describe tab, and then select the Blueprint tab, as illustrated in the following screenshot:

Figure 6.17 – Model blueprint

Here, you can see the workflow steps. As you can see, this blueprint is fairly simple. This is because gradient boost methods are very flexible and do not require a lot of preprocessing. Let's look at another model that did pretty well, the Generalized Additive2 Model (Gamma Loss) blueprint, as illustrated in the following screenshot:

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