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

Accessing the DataRobot API

The programmatic use of DataRobot enables data experts to leverage the platform's efficacies while having the flexibility associated with typical programming. With the API access of DataRobot, data from numerous sources can be integrated for analytic or modeling purposes. This capability is not only limited to the data that's ingested, but also the output of the outcome. For instance, API access makes it possible for a customer risk profiling model to get data from differing sources, such as Google BigQuery, local files, as well as AWS S3 buckets. And in a few lines of codes, the outcomes can update records on Salesforce, as well as those surfaced on PowerBI via a BigQuery table. The strength of this multiple data source integration capability is furthered as this enables the automated, scheduled, end-to-end periodic refresh of model outcomes.

In this preceding case, it becomes possible for the client base to be rescored periodically. Regarding...