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

Scoring and prediction methods

DataRobot provides multiple methods to score datasets using models that have been created. One of the easiest methods is batch scoring via the DataRobot user interface (UI). For this, we need to follow these steps:

  1. Create a file with the dataset to be scored. Given that we are using a public dataset, we will simply use the same dataset to score. In a real project, you will have access to a new dataset for which you want to create predictions. For our purposes, we simply created a copy of our imports-85-data.xlsx dataset file and named it imports-85-data-score.xlsx.
  2. Now, let's select the Predict tab and then the Test Predictions tab for the XGBoost (XGB) models, as shown in the following screenshot:

    Figure 8.1 – Batch scoring

    In the preceding screenshot, you will see that you have an option to drag and drop a new dataset to add the scoring file to the model.

  3. Let's select our imports-85-data-score.xlsx scoring file and...