| 000 -LEADER |
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| fixed length control field |
a |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
250531b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781801814430 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
005.365 |
| Item number |
LOD |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Lo Duca, Angelica |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Comet for Data Science: Enhance your ability to manage and optimize the life cycle of your data science project |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Name of publisher, distributor, etc |
Packt, |
| Date of publication, distribution, etc |
2022 |
| Place of publication, distribution, etc |
Birmingham : |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xviii, 382 p. ; |
| Other physical details |
ill., figures, diagrams, charts, (b & w), |
| Dimensions |
24 cm |
| 365 ## - TRADE PRICE |
|---|
| Price amount |
3499.00 |
| Price type code |
₹ |
| Unit of pricing |
01 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
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. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Industrial applications |
|
|---|
| Topical term or geographic name as entry element |
Project management |
|
|---|
| Topical term or geographic name as entry element |
Model evaluation |
|
|---|
| Topical term or geographic name as entry element |
Natural Language Processing; |
|
|---|
| Topical term or geographic name as entry element |
Matplotlib |
|
|---|
| Topical term or geographic name as entry element |
Scikit-learn |
|
|---|
| Topical term or geographic name as entry element |
Shapley value |
|
|---|
| Topical term or geographic name as entry element |
TensorFlow |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Mendels, Gideon |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Item type |
Books |