Book Image

Comet for Data Science

By : Angelica Lo Duca
Book Image

Comet for Data Science

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.

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Comet for Data Science
Angelica Lo Duca
Aug, 2022 | 402 pages
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Table of Contents (16 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Share Your Thoughts
1
Section 1 – Getting Started with Comet
Section 1 – Getting Started with Comet
Free Chapter
2
Chapter 1: An Overview of Comet
Chapter 1: An Overview of Comet
Technical requirements
Motivation, purpose, and first access to the Comet platform
Getting started with workspaces, projects, experiments, and panels
First use case – tracking images in Comet
Second use case – simple linear regression
Summary
Further reading
3
Chapter 2: Exploratory Data Analysis in Comet
Chapter 2: Exploratory Data Analysis in Comet
Technical requirements
Introducing EDA
Exploring EDA techniques
Using Comet for EDA
Summary
Further reading
4
Chapter 3: Model Evaluation in Comet
Chapter 3: Model Evaluation in Comet
Technical requirements
Introducing model evaluation
Exploring model evaluation techniques
Using Comet for model evaluation
Summary
Further reading
5
Section 2 – A Deep Dive into Comet
Section 2 – A Deep Dive into Comet
6
Chapter 4: Workspaces, Projects, Experiments, and Models
Chapter 4: Workspaces, Projects, Experiments, and Models
Technical requirements
Exploring the Comet UI
Using experiments and models
Exploring other languages supported by Comet
First use case – offline and existing experiments
Second use case – model optimization
Summary
Further reading
7
Chapter 5: Building a Narrative in Comet
Chapter 5: Building a Narrative in Comet
Technical requirements
Discovering the DIKW pyramid
Moving from data to wisdom
Choosing the correct chart type
Using Comet to build a narrative
Summary
Further reading
8
Chapter 6: Integrating Comet into DevOps
Chapter 6: Integrating Comet into DevOps
Technical requirements
Exploring DevOps and MLOps principles and best practices
Combining Comet and DevOps/MLOps
Implementing Docker
Implementing Kubernetes
Summary
Further reading
9
Chapter 7: Extending the GitLab DevOps Platform with Comet
Chapter 7: Extending the GitLab DevOps Platform with Comet
Technical requirements
Introducing the concept of CI/CD
Implementing the CI/CD workflow in GitLab
Integrating Comet with GitLab
Integrating Docker with the CI/CD workflow
Summary
Further reading
10
Section 3 – Examples and Use Cases
Section 3 – Examples and Use Cases
11
Chapter 8: Comet for Machine Learning
Chapter 8: Comet for Machine Learning
Technical requirements
Introducing machine learning
Reviewing the main machine learning models
Reviewing the scikit-learn package
Building a machine learning project from setup to report
Summary
Further reading
12
Chapter 9: Comet for Natural Language Processing
Chapter 9: Comet for Natural Language Processing
Technical requirements
Introducing basic NLP concepts
Exploring the Spark NLP package
Setting up the environment for Spark NLP
Using NLP, from project setup to report building
Summary
Further reading
13
Chapter 10: Comet for Deep Learning
Chapter 10: Comet for Deep Learning
Technical requirements
Introducing basic deep learning concepts
Exploring the TensorFlow package
Using deep learning- from project setup to report building
Summary
Further reading
14
Chapter 11: Comet for Time Series Analysis
Chapter 11: Comet for Time Series Analysis
Technical requirements
Introducing basic concepts related to time series analysis
Exploring the Prophet package
Using time series analysis from project setup to report building
Summary
Further reading
Why subscribe?
15
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Introducing the concept of CI/CD

DevOps best practices permit us to build a pipeline that connects the development phase with the operations phase through different steps. In the previous chapter, you learned how to deploy your Data Science project for the first time. You implemented all the steps manually by building a Docker image and then deploying it in Kubernetes. However, this described procedure does not scale if you perform daily updates to your software.

To automate the integration between the development and operation phases, we should introduce two new concepts, which are CI/CD and SCS.

This section is organized as follows:

  • An overview of CI/CD
  • The concept of an SCS
  • The CI/CD workflow

Let’s start from the first point, which is an overview of CI/CD.

An overview of CI/CD

When using software in production, either a generic app or a machine learning prediction service, you (or other users) may find some bugs in the code or may want...

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