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

Implementing MLOps

DataRobot, through its MLOps suite, provides capabilities to enable users to not only deploy models in production, but govern, monitor, and manage the models in production. In previous chapters, we have looked at how models are deployed on the platform and using the Python API client. MLOps provides an automated model monitoring capability, which tracks the service health, accuracy, and data drift of models in production. The automated real-time monitoring of production models ensures that models have high-quality outputs. Also, when there is a performance degeneration, stakeholders are notified, so action can be taken.

In this section, we will focus on aspects of model monitoring that we didn't cover in Chapter 8, Model Scoring and Deployment, of this book. We looked at how to examine the quality of deployment services, as well as changes in the underline feature distribution between the training and prediction data across time through the service health...