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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

Using time series analysis from project setup to report building

In this section, you will implement a practical example that builds two models to predict the future trend of a time series describing arrivals at tourist accommodation establishments. The dataset shows the trend from 1990 to 2022; thus, it contains a breakpoint in correspondence in April 2020, when the COVID-19 pandemic began.

In the example, you will build two models, one which considers the breakpoint at the beginning of the COVID-19 pandemic and another which does not. You will compare the two models in Comet to establish which one performs better.

The full code of the example described in this section is available at the following link: https://github.com/PacktPublishing/Comet-for-Data-Science/tree/main/11.

You can write the code using the editor or the notebook you prefer. In this example, you will use Deepnote, a popular online notebook, which is fully integrated with Comet.

You will focus on the following...

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