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Comet for Data Science

Comet for Data Science

By : Angelica Lo Duca
4.7 (6)
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Comet for Data Science

Comet for Data Science

4.7 (6)
By: Angelica Lo Duca

Overview of this book

This book provides concepts and practical use cases which can be used to quickly build, monitor, and optimize data science projects. Using Comet, you will learn how to manage almost every step of the data science process from data collection through to creating, deploying, and monitoring a machine learning model. The book starts by explaining the features of Comet, along with exploratory data analysis and model evaluation in Comet. You’ll see how Comet gives you the freedom to choose from a selection of programming languages, depending on which is best suited to your needs. Next, you will focus on workspaces, projects, experiments, and models. You will also learn how to build a narrative from your data, using the features provided by Comet. Later, you will review the basic concepts behind DevOps and how to extend the GitLab DevOps platform with Comet, further enhancing your ability to deploy your data science projects. Finally, you will cover various use cases of Comet in machine learning, NLP, deep learning, and time series analysis, gaining hands-on experience with some of the most interesting and valuable data science techniques available. By the end of this book, you will be able to confidently build data science pipelines according to bespoke specifications and manage them through Comet.
Table of Contents (16 chapters)
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1
Section 1 – Getting Started with Comet
5
Section 2 – A Deep Dive into Comet
10
Section 3 – Examples and Use Cases

Chapter 7: Extending the GitLab DevOps Platform with Comet

When you implement a data science project, you should consider that your model could age due to various factors, such as concept drift or data drift. For this reason, your project will probably need constant updates.

In the previous chapter, you learned the fundamental principles related to DevOps, which allow you to move the project from the test phase to the production phase. However, this is not enough to deal with constant updates efficiently. In fact, you may need to develop an automatic or semi-automatic procedure that allows you to pass from the build/test phase to the production phase easily without too many manual interventions.

In this chapter, you will review the basic concepts behind Continuous Integration and Continuous Delivery (CI/CD), two strategies that permit you to easily and automatically update your code and move from building/testing to production efficiently. You will also learn how you can implement...

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