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 time series forecasting models and understanding their model outcomes

Similar to projects we looked at in Chapter 4, Preparing Data for DataRobot, through to Chapter 8, Model Scoring and Deployment, once we have finished with the initial configurations, we scroll up and click on the Start button. By doing this, DataRobot automatically builds time series models for this project. Before we evaluate the models, it would be useful to understand the nature of the features the platform extracts. DataRobot extracts features from the data that differ considerably from those of other prediction models, as is evident in the following screenshot:

Figure 9.4 – Feature lists

The lists shown under the Feature Lists tab are constructed as part of exploratory data analysis (EDA) and itemize differing lists of features that DataRobot employs in creating models. Many of the feature lists involve derived features, which are created automatically based on properties...