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

Notifications and changing models in production

In this chapter, we have established why the commercial impact of models can decay and ways to track this impact in the DataRobot platform. In cases where the end-to-end prediction process is fully automated and human intervention is limited, it becomes crucial that systems that notify stakeholders of any significant changes in the performance of production models are available. DataRobot can send notifications for significant changes in service health, data drift, and accuracy. These notifications can be set up and configured within the Deployment window:

  1. From the Settings tab, select Notifications. As shown in Figure 13.12, three options are presented: notifications being sent for all events, notifications for critical events, and no notifications being sent. Notifications for all events are sent by email; all changes to the deployments are emailed to the owner:

    Figure 13.12 – Deployment Notifications setup

    In Figure 13...