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

Determining predictions, actions, and consequences for Responsible AI

After the model is built and deployed in DataRobot, it might seem that our job is done—but not so fast. You should start analyzing what the predictions profile looks like and start discussing with users and stakeholders the details of actions to be taken. The models you have helped build are likely to introduce many changes in your system and will impact other people. It is therefore important to try to make sure that these impacts are not negative. Making sure that your models will not cause harm is called Responsible AI. This will build upon the work you did during the understanding phase through various diagrams.

Just as in previous sections we saw how a causal diagram helps you to relate features to a target, we can also see how the target affects other parts of the system. The diagram should reveal how the target impacts key objectives or outcomes; it should also reveal key feedback loops that will...