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

Aggregating data for modeling

From the previous chapters, you might remember that machine learning algorithms expect the dataset to be in a specific form and it needs to be in one table. The data needed for this table, however, could reside in multiple sources. Hence, one of the first things you need to do is to aggregate data from multiple sources. This is often done using SQL or Python. Recently, DataRobot has added the capability to add multiple datasets into a project and then aggregate this data within DataRobot. Please note that there are still some data cleansing operations that you might have to do outside of DataRobot, so if you want to use the aggregation capabilities of DataRobot, you need to do cleansing operations prior to bringing this data into DataRobot. We cover data cleansing in the following section. If you choose to do data aggregation inside DataRobot, you have to make sure to do this at the very start of the project (Figure 4.4):

Figure 4...