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

Getting to the root of the business problem

Some problems are easy to solve, while others prove to be much harder. One of the reasons this happens is that when a problem's symptoms appear somewhere else and after some delay, then it is very difficult to know where the problem really is. By definition, the symptoms are clearly visible—they are explicit and you can easily collect data about them. The underlying problem, on the other hand, is happening in some other department or building and is not visible because it is not causing immediate pain. Most likely, no data is being collected about the root problem, or it might be too hard to collect that data. Given the nature of ML, it is almost a given that all the data you are getting is about symptoms. If you are lucky, you might get some data about the root problem as well (although you will not know it).

One of the ways to get started is by using an old method called five whys, which basically involves asking the question...