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 geospatial, text, and image data

Just like we use different senses to holistically understand objects around us, a machine learning (ML) model also benefits from data coming from different types of sensors and sources. Having only one type of data (for instance, numeric or categorical) limits the level of understanding, predictability, and robustness of a model. In this section, we will present a more in-depth discussion of the business importance of different data types in building models, the associated challenges, and the preprocessing steps necessary to mitigate these challenges.

Geospatial AI

Geospatial understanding has had long-standing implications for decision-making in certain industries, including mineral exploitation, insurance, retail, and real estate. While the commercial importance of data science is well established, location-based AI is just beginning to gain recognition. The use of ML in improving business performance has brought...