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

Defining and setting up time series projects

In Chapter 4, Preparing Data for DataRobot, through to Chapter 8, Model Scoring and Deployment, we explored the creation, understanding, scoring, and deployment of basic models in DataRobot. We saw that DataRobot automatically built several models for us and we could then score a dataset using these built models. Further, after we have chosen a model that best aligns with our needs, DataRobot provides us a process to deploy our selected model. Due to the difference between time series modeling and other forms of predictive modeling, we will explore in this section how to mitigate problems by effectively defining and setting up time series projects in DataRobot.

The dataset we will use to explore the use of time series modeling with DataRobot is the Appliances energy prediction dataset that we explored in Chapter 4, Preparing Data for DataRobot. The goal of the project is to predict energy usage. This energy usage time series dataset...