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

Building ensemble models

It is well known that ensembles of models tend to perform better and also tend to be more robust. DataRobot provides the capability to automatically build ensemble models; however, this does require some trade-offs. For example, ensemble models take more time and computational resources to build and deploy, and they also tend to be more opaque. This is the reason we did not start off by building ensemble models. Once you have built several models and you are interested in ways of improving your model accuracy, you can decide to build ensembles. As we saw in the previous sections, we have to explicitly select the option to build ensembles, and that also means that we cannot compute SHAP values. Let's look at how this is done. Let's first go to the project list page, which shows all of your current projects, as illustrated in the following screenshot:

Figure 6.24 – Project list

Here, we will select the Actions icon for...