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)
1
Section 1 – Getting Started with Comet
5
Section 2 – A Deep Dive into Comet
10
Section 3 – Examples and Use Cases

Discovering the DIKW pyramid

When you want to build a story from data, you first need to explore your data to understand which questions it can answer, as well as which data is relevant for your project. You already learned how to perform EDA in Chapter 2, Exploratory Data Analysis in Comet, so in this chapter, we suppose that you already have relevant data and, in general, have an idea of which questions your data can answer.

To build a story from data, you first need to think about the audience that will read your story. When you write a story, your preliminary purpose should be one of the following:

  • Entertaining the audience
  • Informing the audience
  • Teaching something to the audience

The effect of your story should be calling the audience to action. To achieve your goal, you need to transform your data by interpreting it, enriching it with contextual information, and finally, linking it to an ethical model that calls the audience to action.

The Data...