Wow! eBook
Comet for Data Science: Enhance your ability to manage and optimize the life cycle of your data science project
Author(s): Angelica Lo Duca (Author)
- Publisher: Packt Publishing
- Publication Date: 26 Aug. 2022
- Language: English
- Print length: 402 pages
- ISBN-10: 1801814430
- ISBN-13: 9781801814430
Book Description
Gain the key knowledge and skills required to manage data science projects using Comet
Key Features
- Discover techniques to build, monitor, and optimize your data science projects
- Move from prototyping to production using Comet and DevOps tools
- Get to grips with the Comet experimentation platform
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
Who this book is for
This book is for anyone who has programming experience, and wants to learn how to manage and optimize a complete data science lifecycle using Comet and other DevOps platforms. Although an understanding of basic data science concepts and programming concepts is needed, no prior knowledge of Comet and DevOps is required.
Table of Contents
- An Overview of Comet
- Exploratory Data Analysis in Comet
- Model Evaluation in Comet
- Workspaces, Projects, Experiments and Models
- Building a Narrative in Comet
- Integrating Comet into DevOps
- Extending the Gitlab DevOps Platform with Comet
- Comet for Machine Learning
- Comet for Natural Language Processing
- Comet for Deep Learning
- Comet for Time Series Analysis
Editorial Reviews
Review
“Managing data science projects is difficult―Complex pipelines are tricky to implement without error, can present problems when you try to share them with others, and are not usually built with reproducibility in mind. Approaches to management are far from cut-and-dried, in part due to projects being alive, meaning they must continually integrate new data and be re-evaluated.
Comet can help with all of this since it’s made to help manage experiments, evaluate models, and version data, all while making reproducibility and collaboration straightforward. Comet for Data Science, masterfully written by Angelica Lo Duca, provides a bottom-up approach to learning how to manage data science projects with Comet, and does so without assuming you have any prior experience with data science project management. Every aspect of project management is well-explained, so you’ll come away with the confidence to use Comet to manage your own practical data science projects right away.
This book is a welcome addition to my data science bookshelf.”
—
Matthew Mayo, Editor-in-Chief at KDnuggets
About the Author
Angelica Lo Duca is a researcher at the Institute of Informatics and Telematics of the National Research Council, Italy. She is also an external professor of Data Journalism at the University of Pisa. Her research includes Data Science, Data Journalism, and Web Applications. She used to work on Network Security, Semantic Web, Linked Data and Blockchain. She has published more than 40 scientific papers at national and international conferences and journals, and participated in many international projects and events, including as a member of the Program Committee.
