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

Conceptual introduction to time series forecasting modeling

The dynamic nature of the commercial environment makes time a pivot resource for business success. As a result, businesses need to account for the time factor in their decision-making. Changes occur within commercial settings at a high pace, which makes it pertinent for organizations to take rapid yet considered actions. Analytic technology provides organizations with tools that enable forecasting of the future so that decision-makers have crucial time in hand to ensure their decision aligns with their organizational objectives. Organizations use time-specific data to predict the volume of sales in a future period. Other writers have differentiated time series modeling from forecasting models. In this chapter, we have used the term interchangeably and consider time series forecasting to involve the use of advanced analytics to gain insights that guide business decisions leveraging time-based data.

Time series forecasting...