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

Chapter 11: Working with Geospatial Data, NLP, and Image Processing

In this book thus far, we have focused mainly on numeric and categorical features. This is not always the case in big data, as with big data comes an increasing data variety. Image, text, and geospatial data is becoming increasingly valuable in gaining insight and providing solutions to the most complex problems. Recently, for instance, location-based data has been used to improve the effectiveness of advertising campaigns. For example, different ads can be shown to users according to their location; if they are coffee lovers and close to coffee shops, push notifications could be sent to their mobile devices. In other cases, chatbots, based on advanced text analytics or natural language processing, provide businesses with an efficient and effective avenue to solve customer problems. What is most interesting and an emerging approach to solving commercial problems is the use of multimodal datasets, which combine different...