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

DataRobot provides us with a unique capability to rapidly develop models. With this platform, data scientists can combine the benefits of DataRobot and the flexibilities of open programming. In this chapter, we explored ways to access the credentials needed to programmatically use DataRobot. Using the Python client, we demonstrated ways in which data can be ingested and how basic projects can be created. We started building models for more complex problems. We created model factories as well as one versus all models. Finally, we demonstrated how models can be deployed and used to score data.

One of the key advantages of programmatically using DataRobot is the ability to ingest data from numerous sources, score them, and store them in the relevant sources. This makes it possible to carry out end-to-end dataset scoring. It becomes possible for a system to be set up to score models periodically. With this comes numerous data quality and model monitoring concerns. The next chapter...