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

Operationalizing and generating value

Operationalizing a model in your infrastructure can be a complicated undertaking. There are some aspects of deployment that are made simple by DataRobot, but there are other parts of deployment that are outside the scope of DataRobot and can be quite challenging. Let's discuss the tasks that are part of this process, as follows:

  • Deploying a model as an application programming interface (API): One of the very first tasks is to deploy your model as an API so that it can serve predictions as needed. You will have to decide whether this needs to be a batch or real-time operation. DataRobot automates much of the task of setting this up, and you can have an API serving predictions in minutes.
  • Integration and testing with business systems: Having an API is only part of the story—you will now need to integrate this API into your business systems. Sometimes, you can serve up predictions to users via standalone Excel files or web...