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 4: Preparing Data for DataRobot

This chapter covers tasks relating to preparing data for modeling. While the tasks themselves are relatively straightforward, they can take up a lot of time and can sometimes cause frustration. Just know that if you feel this way, you are not alone. This is pretty normal. This is also where you will begin to notice that things are a bit different from your experience in an academic setting. Data will almost never arrive in a form that's suitable for modeling, and it is a mistake to assume that the data you have received is in good condition and of good quality.

Most real-world problems do not come with a ready-made dataset that you can start processing and use to build models. Most likely you will need to stitch data together from multiple disparate sources. Depending on the data, DataRobot might perform data preparation and cleansing tasks automatically, or you might have to do some of these on your own. This chapter covers concepts...