Comet for Data Science: Enhance your ability to manage and optimize the life cycle of your data science project

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.

獲得使用Comet管理數據科學項目所需的關鍵知識和技能。

主要特點：
- 探索構建、監控和優化數據科學項目的技術
- 使用Comet和DevOps工具從原型開發到生產環境
- 熟悉Comet實驗平台

書籍描述：
本書提供了概念和實用案例，可用於快速構建、監控和優化數據科學項目。使用Comet，您將學習如何管理從數據收集到創建、部署和監控機器學習模型的數據科學過程的幾乎每一個步驟。

本書首先解釋了Comet的特點，以及在Comet中的探索性數據分析和模型評估。您將看到Comet讓您可以根據需求選擇多種編程語言。接下來，您將專注於工作區、項目、實驗和模型。您還將學習如何使用Comet提供的功能從數據中建立敘述。然後，您將回顧DevOps背後的基本概念，以及如何通過Comet擴展GitLab DevOps平台，進一步提升部署數據科學項目的能力。最後，您將涵蓋Comet在機器學習、自然語言處理、深度學習和時間序列分析中的各種用例，並獲得一些最有趣和有價值的數據科學技術的實踐經驗。

通過閱讀本書，您將能夠自信地根據定制規格構建數據科學流程並通過Comet進行管理。

學到的內容：
- 使用正確的數據準備項目
- 理解不同機器學習算法的目的
- 開始使用Comet來管理和監控流程
- 理解Comet的工作原理並充分利用它
- 瞭解如何使用Comet進行機器學習
- 發現如何將Comet與GitLab集成
- 使用Comet進行自然語言處理、深度學習和時間序列分析

適合閱讀對象：
本書適合具有編程經驗並希望學習如何使用Comet和其他DevOps平台管理和優化完整數據科學生命周期的人。雖然需要對基本數據科學概念和編程概念有一定的理解，但不需要事先了解Comet和DevOps。

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.

Angelica Lo Duca是意大利國家研究委員會信息學與遠程通信研究所的研究員。她還是比薩大學數據新聞學的外部教授。她的研究領域包括數據科學、數據新聞學和網絡應用。她曾在網絡安全、語義網、關聯數據和區塊鏈方面工作。她在國內外會議和期刊上發表了40多篇科學論文，並參與了許多國際項目和活動，包括作為程序委員會成員。

1. An Overview of Comet
2. Exploratory Data Analysis in Comet
3. Model Evaluation in Comet
4. Workspaces, Projects, Experiments and Models
5. Building a Narrative in Comet
6. Integrating Comet into DevOps
7. Extending the Gitlab DevOps Platform with Comet
8. Comet for Machine Learning
9. Comet for Natural Language Processing
10. Comet for Deep Learning
11. Comet for Time Series Analysis

1. Comet 概述
2. Comet 中的探索性數據分析
3. Comet 中的模型評估
4. 工作區、專案、實驗和模型
5. 在 Comet 中建立敘述
6. 將 Comet 整合到 DevOps 中
7. 通過 Comet 擴展 Gitlab DevOps 平台
8. Comet 用於機器學習
9. Comet 用於自然語言處理
10. Comet 用於深度學習
11. Comet 用於時間序列分析