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

Making predictions with time series models

DataRobot provides us with tools to make predictions pain-free. There are two approaches to making predictions for time series. For small datasets under 1 gigabyte (GB), predictions could be made using the Make Predictions tab on the Leaderboard feature. This involves setting up and uploading a prediction dataset, then scoring it within the Drag and drop a new dataset user interface (UI) functionality. For significantly larger datasets, models need to be deployed and predictions are made using an application programming interface (API). In this chapter, we will cover the first approach to making predictions. With DataRobot, general model deployments and working with APIs are extensively discussed in Chapter 12, DataRobot Python API.

The leaderboard's drag-and-drop approach to scoring models for time series models somewhat differs from those of traditional models, as seen in Chapter 8, Model Scoring and Deployment. When the Make Predictions...