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

A conceptual introduction to recommender systems

Businesses have a long-standing history of recommending their products or services to customers. For instance, walk into a bookshop and you are likely to see a list of popular books bought by other customers. This is a simple kind of recommendation system, as it gives buyers a snapshot of potential products to purchase.

In a bid to win in the digital economy, businesses are becoming increasingly customer-centric. Customer centricity implies that companies aim to put the needs of the customer first. Still, with the needs of customers being as diverse as the customers themselves, businesses need to take a unique approach in putting forward their products. This explains, in part, the failings of popularity-based recommendation systems, as they fail to consider the unique profiles of buyers. As such, with growing digitalization, increased business offerings, and a growing diversity of customers' needs, this approach is unlikely to...