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

by Angelica Lo Duca, Gideon Mendels

Length: 402 pages
Edition: 1
Language: English
Publisher: Packt Publishing
Publication Date: 2022-08-26
ISBN-10: 1801814430
ISBN-13: 9781801814430
Sales Rank: #777431 (See Top 100 Books)

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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.

Comet for Data Science
Foreword
Contributors
About the author
About the reviewers
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
Section 1 – Getting Started with Comet
Chapter 1: An Overview of Comet
    Technical requirements
        comet-ml
        matplotlib
        numpy
        pandas
        scikit-learn
    Motivation, purpose, and first access to the Comet platform
        Motivation
        Purpose
        First access to the Comet platform
    Getting started with workspaces, projects, experiments, and panels
        Workspaces
        Projects
        Experiments
        Panels
    First use case – tracking images in Comet
        Downloading the dataset
        Dataset cleaning
        Building the visualizations
        Integrating the graphs in Comet
        Building a panel
    Second use case – simple linear regression
        Initializing the context
        Defining, fitting, and evaluating the model
        Showing results in Comet
    Summary
    Further reading
Chapter 2: Exploratory Data Analysis in Comet
    Technical requirements
        pandas Profiling
        seaborn
        sweetviz
    Introducing EDA
        Problem setting
        Data preparation
        Preliminary data analysis
        Preliminary results
    Exploring EDA techniques
        Loading and preparing the dataset
        Non-visual EDA
        Visual EDA
    Using Comet for EDA
        Comet logs
        Panels
        Comet Report
    Summary
    Further reading
Chapter 3: Model Evaluation in Comet
    Technical requirements
    Introducing model evaluation
        Data splitting
        Choosing metrics
    Exploring model evaluation techniques
        Loading and preparing the dataset
        Regression
        Classification
        Clustering
    Using Comet for model evaluation
        Comet Log
        Comet Dashboard
        Registry
        Reports
    Summary
    Further reading
Section 2 – A Deep Dive into Comet
Chapter 4: Workspaces, Projects, Experiments, and Models
    Technical requirements
        Python
        R
        Java
    Exploring the Comet UI
        Workspaces
        Projects
    Using experiments and models
        Experiments
        Models
    Exploring other languages supported by Comet
        R
        Java
    First use case – offline and existing experiments
        Running an offline experiment
        Continuing an existing experiment
        Improving an existing experiment offline
    Second use case – model optimization
        Creating and configuring an Optimizer
        Optimizing the model
        Showing the results in Comet
    Summary
    Further reading
Chapter 5: Building a Narrative in Comet
    Technical requirements
    Discovering the DIKW pyramid
        Data
        Information
        Knowledge
        Wisdom
    Moving from data to wisdom
        Turning data into information
        Turning information into knowledge
        Turning knowledge into wisdom
    Choosing the correct chart type
        A line chart
        A bar chart
        An area chart
        A pie chart
    Using Comet to build a narrative
        Using JavaScript panels
        Building advanced reports
    Summary
    Further reading
Chapter 6: Integrating Comet into DevOps
    Technical requirements
        Python
        Docker
        Kubernetes
    Exploring DevOps and MLOps principles and best practices
        The DevOps life cycle
        Moving from DevOps to MLOps
    Combining Comet and DevOps/MLOps
        Comet in the DevOps life cycle
        Setting up the Comet REST API service
        Using the Comet REST API
    Implementing Docker
        Overview of Docker
        Running Comet in Docker container
    Implementing Kubernetes
        The Kubernetes architecture
        Configuring Kubernetes
        Deploying a local Kubernetes cluster
    Summary
    Further reading
Chapter 7: Extending the GitLab DevOps Platform with Comet
    Technical requirements
        Python
        Git client
    Introducing the concept of CI/CD
        An overview of CI/CD
        The concept of an SCS
        The CI/CD workflow
    Implementing the CI/CD workflow in GitLab
        Creating/modifying a GitLab project
        Exploring GitLab's internal structure
        Exploring GitLab concepts for CI/CD
        Building the CI/CD pipeline
        Creating a release
    Integrating Comet with GitLab
        Running Comet in the CI/CD workflow
        Using webhooks
    Integrating Docker with the CI/CD workflow
    Summary
    Further reading
Section 3 – Examples and Use Cases
Chapter 8: Comet for Machine Learning
    Technical requirements
        shap
    Introducing machine learning
        Exploring the machine learning workflow
        Classifying machine learning systems
        Exploring machine learning challenges
        Explaining machine learning models
    Reviewing the main machine learning models
        Supervised learning
        Unsupervised learning
    Reviewing the scikit-learn package
        Preprocessing
        Dimensionality reduction
        Model selection
        Supervised and unsupervised learning
    Building a machine learning project from setup to report
        Reviewing the scenario
        Selecting the best model
        Calculating the SHAP value
        Building the final report
    Summary
    Further reading
Chapter 9: Comet for Natural Language Processing
    Technical requirements
    Introducing basic NLP concepts
        Exploring the NLP workflow
        Classifying NLP systems
        Exploring NLP challenges
        Reviewing the most popular models’ hubs
    Exploring the Spark NLP package
        Introducing the Spark NLP package
        Integrating Spark NLP with Comet
    Setting up the environment for Spark NLP
        Installing Java
        Installing Scala (optional)
        Installing Apache Spark
        Installing PySpark and Spark NLP
    Using NLP, from project setup to report building
        Configuring the environment
        Loading the dataset
        Implementing a pretrained pipeline
        Logging results in Comet
        Using a custom pipeline
        Building the final report
    Summary
    Further reading
Chapter 10: Comet for Deep Learning
    Technical requirements
        gradio
        tensorFlow
    Introducing basic deep learning concepts 
        Introducing neural networks
        Exploring the difference between deep learning and neural networks
        Classifying deep learning networks  
    Exploring the TensorFlow package 
        Introducing the TensorFlow package
        Integrating TensorFlow with Comet
    Using deep learning- from project setup to report building
        Introducing Gradio
        Loading the dataset
        Implementing a basic model
        Exploring results in Comet 
        Building a prediction interface
        Building the final report
    Summary
    Further reading
Chapter 11: Comet for Time Series Analysis
    Technical requirements
        Prophet
        statsmodels
    Introducing basic concepts related to time series analysis 
        Loading a time series in Python
        Checking whether a time series is stationary
        Exploring the time series components
        Identifying breakpoints in a time series
    Exploring the Prophet package 
        Introducing the Prophet package
        Integrating Prophet with Comet
    Using time series analysis from project setup to report building
        Configuring the Deepnote environment
        Loading and preparing the dataset
        Checking stationarity in data
        Building the models
        Exploring results in Comet 
        Building the final report
    Summary
    Further reading
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