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

Advanced topics in time series modeling

In this chapter, we have learned how to configure, build and make predictions with basic time series forecasting models in DataRobot. In the preceding section, our attention was focused on building models that have one-time series. However, you could have a situation where you might have to make multi-time series predictions. Within the context of our energy utilization problem, we might want to forecast the usage of lights and appliances. Elsewhere, an energy company might want to forecast energy usage for differing cities or households within the same model. We will now take a deep dive into solving problems of this nature. Also, we will explore future ways other advanced approaches may be used in assessing our time series models. Finally, we will acknowledge the role of scheduled events on time series and highlight the provisions made by DataRobot to handle this possibility.

The dataset used for this project highlights the energy usage...