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 highlighted the value of establishing a framework guiding the use of ML models in businesses. ML governance capability supports users in ensuring that ML models continue to deliver commercial value while meeting regulatory expectations. Also, we set controls for what different levels of stakeholders can do with ML deployments. In some industries, there is a need to seriously consider the impact of bias in any decision process. Because ML models are based on data that might have been affected by human bias, it is possible that these models will compound such bias. As such, we explored ways to mitigate ML bias during and after model development.

We also examined the effects features have on the outcome variable. Such changes could have a critical bearing on business outcomes, hence the need to monitor the performance of model outcomes in production. During this chapter, we explored ways the performance of models could be assessed over time. Importantly...