book

Comet for Data Science

Name: Comet for Data Science
Author: Angelica Lo Duca
ISBN: 9781801814430

by Angelica Lo Duca

August 2022

Beginner to intermediate

402 pages

8h 42m

English

Packt Publishing

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Comet for Data Science
ForewordContributorsAbout the authorAbout the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesDownload the color imagesConventions usedGet in touchShare Your Thoughts
Section 1 – Getting Started with Comet
Chapter 1: An Overview of Comet
Technical requirementscomet-mlmatplotlibnumpypandasscikit-learnMotivation, purpose, and first access to the Comet platformMotivationPurposeFirst access to the Comet platformGetting started with workspaces, projects, experiments, and panelsWorkspacesProjectsExperimentsPanelsFirst use case – tracking images in CometDownloading the datasetDataset cleaningBuilding the visualizationsIntegrating the graphs in CometBuilding a panelSecond use case – simple linear regressionInitializing the contextDefining, fitting, and evaluating the modelShowing results in CometSummaryFurther reading
Chapter 2: Exploratory Data Analysis in Comet
Technical requirementspandas ProfilingseabornsweetvizIntroducing EDAProblem settingData preparationPreliminary data analysisPreliminary resultsExploring EDA techniquesLoading and preparing the datasetNon-visual EDAVisual EDAUsing Comet for EDAComet logsPanelsComet ReportSummaryFurther reading
Chapter 3: Model Evaluation in Comet
Technical requirementsIntroducing model evaluationData splittingChoosing metricsExploring model evaluation techniquesLoading and preparing the datasetRegressionClassificationClusteringUsing Comet for model evaluationComet LogComet DashboardRegistryReportsSummaryFurther reading
Section 2 – A Deep Dive into Comet
Chapter 4: Workspaces, Projects, Experiments, and Models
Technical requirementsPythonRJavaExploring the Comet UIWorkspacesProjectsUsing experiments and modelsExperimentsModelsExploring other languages supported by CometRJavaFirst use case – offline and existing experimentsRunning an offline experimentContinuing an existing experimentImproving an existing experiment offlineSecond use case – model optimizationCreating and configuring an OptimizerOptimizing the modelShowing the results in CometSummaryFurther reading
Chapter 5: Building a Narrative in Comet
Technical requirementsDiscovering the DIKW pyramidDataInformationKnowledgeWisdomMoving from data to wisdomTurning data into informationTurning information into knowledgeTurning knowledge into wisdomChoosing the correct chart typeA line chartA bar chartAn area chartA pie chartUsing Comet to build a narrativeUsing JavaScript panelsBuilding advanced reportsSummaryFurther reading
Chapter 6: Integrating Comet into DevOps
Technical requirementsPythonDockerKubernetesExploring DevOps and MLOps principles and best practicesThe DevOps life cycleMoving from DevOps to MLOpsCombining Comet and DevOps/MLOpsComet in the DevOps life cycleSetting up the Comet REST API serviceUsing the Comet REST APIImplementing DockerOverview of DockerRunning Comet in Docker containerImplementing KubernetesThe Kubernetes architectureConfiguring KubernetesDeploying a local Kubernetes clusterSummaryFurther reading

Chapter 7: Extending the GitLab DevOps Platform with Comet
Technical requirementsPythonGit clientIntroducing the concept of CI/CDAn overview of CI/CDThe concept of an SCSThe CI/CD workflowImplementing the CI/CD workflow in GitLabCreating/modifying a GitLab projectExploring GitLab's internal structureExploring GitLab concepts for CI/CDBuilding the CI/CD pipelineCreating a releaseIntegrating Comet with GitLabRunning Comet in the CI/CD workflowUsing webhooksIntegrating Docker with the CI/CD workflowSummaryFurther reading
Section 3 – Examples and Use Cases
Chapter 8: Comet for Machine Learning
Technical requirementsshapIntroducing machine learningExploring the machine learning workflowClassifying machine learning systemsExploring machine learning challengesExplaining machine learning modelsReviewing the main machine learning modelsSupervised learningUnsupervised learningReviewing the scikit-learn packagePreprocessingDimensionality reductionModel selectionSupervised and unsupervised learningBuilding a machine learning project from setup to reportReviewing the scenarioSelecting the best modelCalculating the SHAP valueBuilding the final reportSummaryFurther reading
Chapter 9: Comet for Natural Language Processing
Technical requirementsIntroducing basic NLP conceptsExploring the NLP workflowClassifying NLP systemsExploring NLP challengesReviewing the most popular models’ hubsExploring the Spark NLP packageIntroducing the Spark NLP packageIntegrating Spark NLP with CometSetting up the environment for Spark NLPInstalling JavaInstalling Scala (optional)Installing Apache SparkInstalling PySpark and Spark NLPUsing NLP, from project setup to report buildingConfiguring the environmentLoading the datasetImplementing a pretrained pipelineLogging results in CometUsing a custom pipelineBuilding the final reportSummaryFurther reading
Chapter 10: Comet for Deep Learning
Technical requirementsgradiotensorFlowIntroducing basic deep learning concepts Introducing neural networksExploring the difference between deep learning and neural networksClassifying deep learning networks Exploring the TensorFlow package Introducing the TensorFlow packageIntegrating TensorFlow with CometUsing deep learning- from project setup to report buildingIntroducing GradioLoading the datasetImplementing a basic modelExploring results in Comet Building a prediction interfaceBuilding the final reportSummaryFurther reading
Chapter 11: Comet for Time Series Analysis
Technical requirementsProphetstatsmodelsIntroducing basic concepts related to time series analysis Loading a time series in PythonChecking whether a time series is stationaryExploring the time series componentsIdentifying breakpoints in a time seriesExploring the Prophet package Introducing the Prophet packageIntegrating Prophet with CometUsing time series analysis from project setup to report buildingConfiguring the Deepnote environmentLoading and preparing the datasetChecking stationarity in dataBuilding the modelsExploring results in Comet Building the final reportSummaryFurther reading
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Content preview from Comet for Data Science

Chapter 6: Integrating Comet into DevOps

Model deployment and monitoring are the last two steps in a data science project life cycle. The former permits you to move your project from testing to production, and the latter provides with you all the strategies and tools to ensure that your project is running without errors, secure, and updated.

You can implement model deployment by adopting a particular philosophy, called DevOps. DevOps (short for Development and Operations) is a set of best practices that permit software developers and operations teams to collaborate during the whole software project life cycle to improve software development, speed, and efficiency through automatic techniques.

As you have already learned from the previous chapters, ...

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Publisher Resources

ISBN: 9781801814430

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

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IT/Ops

Security

Design

Business

Soft Skills

Comet for Data Science

by Angelica Lo Duca

Chapter 6: Integrating Comet into DevOps

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.