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

Technical requirements

For the analysis and modeling that will be carried out in this chapter, you will need access to the DataRobot software. Jupyter Notebook is crucial for this chapter as most of the interactions with DataRobot will be carried out from the console. Your Python version should be 2.7 or 3.4+. Now, let's look at the dataset that will be utilized in this chapter.

Check out the following video to see the Code in Action at https://bit.ly/3wV4qx5.

Automobile Dataset

The automobile dataset can be accessed at the UCI Machine Learning Repository ( https://archive.ics.uci.edu/ml/datasets/Automobile). Each row in this dataset represents a specific automobile. The features (columns) describe its characteristics, risk rating, and associated normalized losses. Even though it is a small dataset, it has many features that are numerical as well as categorical. Its features are described on its web page and the data is provided in.csv format.

Dataset Citation

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