Book Image

Comet for Data Science

By : Angelica Lo Duca
Book Image

Comet for Data Science

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)
Section 1 – Getting Started with Comet
Section 2 – A Deep Dive into Comet
Section 3 – Examples and Use Cases

Technical requirements

We will run all the experiments and codes in this chapter using Python 3.8. You can download it from the official website,, choosing the 3.8 version.

The examples described in this chapter use the following Python packages:

  • comet-ml 3.23.0
  • pandas 1.3.4
  • scikit-learn 1.0

We already described these packages and how to install them in Chapter 1, An Overview of Comet, so please refer back to that for further details on installation.

In addition, the running examples will need other specific requirements, which will be described in the Setting up the environment for Spark NLP section of this chapter.

Now that you have installed all the software needed in this chapter, let’s look at how to use Comet for NLP, starting by reviewing some basic concepts.