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

Technical requirements

Most of the analysis and modeling carried out in this chapter requires access to the DataRobot software. Some manipulations were carried out using other tools, including MS Excel. The dataset utilized in this chapter is the House Dataset.

House Dataset

The House Dataset can be accessed at Eman Hamed Ahmed's GitHub account (https://github.com/emanhamed). Each row in this dataset represents a specific house. The initial feature set describes its characteristics, price, zip code, images of the bedroom, bathroom, kitchen, and frontal view. There was no missing data. We went on to develop text descriptions for each house, based on the number of bedrooms, bathrooms, city, country, state, and actual size of the property. Elsewhere, the ZIP codes were converted into latitude and longitude, which were added to the dataset as columns. More information on the base features is provided at the GitHub link and the data is provided in .csv format.

Dataset Citation...