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

Summary

In this chapter, we learned how to use models after training. We discussed the methods that are used to score a dataset and also methods that are used for analyzing the resulting outputs. We also covered methods and considerations for turning predictions into actions or decisions. This is a critical step whereby you have to engage with your business stakeholders to make sure that introducing this model will not cause unforeseen problems. This is also the time to work on change management tasks such as communicating changes to people who are impacted by the change and ensure that users are trained in the new process and know how to use the new capabilities.

We then discussed how to use DataRobot capabilities to rapidly deploy a model and then monitor the model performance. It is easy to underestimate the importance of this capability. Model deployment and monitoring are not easy, and many organizations spend a lot of time and effort trying to deploy a model. Hopefully, we...