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

Data preparation

Before an algorithm can be applied to a dataset, the dataset needs to fit a certain pattern. The dataset also needs to be free of errors. Certain methods and techniques are used to ensure that the dataset is ready for the algorithms, and this will be the focus of this section.

Supervised learning dataset

Since DataRobot mostly works with supervised learning problems, we will only focus on datasets for supervised machine learning (other types will be covered in a later section). In a supervised machine learning problem, we provide all the answers as part of the dataset. Imagine a table of data where each row represents a set of clues with their corresponding answers (Figure 2.1):

Figure 2.1 – Supervised learning dataset

This dataset is made up of columns that contain clues (these are called features), and there is a column with the answers (this is called target). Given a dataset that looks like this, the algorithm learns how to...