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

Monitoring deployed models

As you will have guessed by now, the job of the data science team does not end once a model is deployed. We now have to monitor this model to see how it is performing, whether it is working as intended, and if we need to intervene and make any changes. We'll proceed as follows:

  1. To see how that works, let's click on the Predictions tab, as shown in the following screenshot:

    Figure 8.20 – Making predictions using the deployed model

  2. We can now upload a dataset to be scored, by dragging and dropping a file (here, we will use the same file that we used before during model training) into the Prediction source box. We can now see other options becoming available, as shown in the following screenshot:

    Figure 8.21 – Computing predictions for a dataset

  3. After selecting the options, we can click on the Compute and download predictions button. After DataRobot finishes the computations, we will see the output file becoming available...