Comet For Data Science Enhance Your Ability To Manage And Optimize The Life Cycle Of Your Data Science Project 1st Edition Angelica Lo Duca by Angelica Lo Duca 9781801814430, 1801814430 instant download after payment.

Gain the key knowledge and skills required to manage data science projects using Comet
Book Description
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.
What You Will Learn
• Prepare for your project with the right data
• Understand the purposes of different machine learning algorithms
• Get up and running with Comet to manage and monitor your pipelines
• Understand how Comet works and how to get the most out of it
• See how you can use Comet for machine learning
• Discover how to integrate Comet with GitLab
• Work with Comet for NLP, deep learning, and time series analysis