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 using a multimodal dataset on DataRobot

After building a model, there are many ways to make predictions on a DataRobot. For this use case, we will illustrate the prediction capability using the Make Prediction method, which is available within the Predict tab. We initially create a prediction ZIP file dataset using the step outline in the Defining and setting up multimodal data in DataRobot section of this chapter. The developed prediction dataset is either dragged and dropped into the highlighted area or locally imported. As seen in Figure 11.12, we select the features we are interested in, including the prediction dataset:

Figure 11.14 – Making a prediction from multimodal datasets

In this illustration, we selected House_id, FullDescription, Bedrooms, City, and State. We can also see that the prediction dataset has 400 houses. Finally, Compute predictions is selected to make predictions. When predictions have been completed, they...