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 the system context

All problems arise within the context of a system. A system could be a single cell of an organism, a global population, or the entire economy. In the same way, all solutions need to fit into a system. A technological solution (for example, an AI solution) will typically require changes to processes, people, skills, other IT systems, or even the business model for it to be effective. For an organization, the system could be its entire supply chain, competitors, and customers. Given that a system's definition can be very broad, it is generally advisable that you imagine a system to be broader than the problems you are considering. You want all the components or agents that your problem touches to be part of the system context. Defining the system boundary is part art and part science, and it is an iterative process. Given that you will be looking at the system from a broader perspective, this also means that the same system context will be valid...