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

Governing models

Organizations using ML governance define a framework of rules and controls for managing the ML workflows pertaining to model development, production, and post-production monitoring. The commercial importance of ML is well established. Still, only a fraction of companies investing in ML are realizing the benefits. Some establishments have struggled to ensure that the outcomes of ML projects are well aligned with their strategic direction. Importantly, many organizations are subject to regulations, such as the recently implemented General Data Protection Regulation within the European Union and European Economic Area, which affect the use of these models and their outputs. Businesses, in general, need to steer their ML use to ensure regulatory requirements are satisfied and strategic goals and values are continually realized.

Having an established governance framework in place ensures that data scientists can focus on the innovative part of their role, which is solving...