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The Ops Compendium
  • The Ops Compendium
  • Definitions
    • Ops Definition Comparisons
  • ML & DL Compendium
  • MLOps
    • MLOps Intro
    • MLOps Teams
    • MLOps Literature
    • MLOps Course
    • MLOps Patterns
    • ML Experiment Management
    • ML Model Monitoring & Alerts
    • MLOps Tools
    • MLOps Deployment
    • Feature Stores & Feature Pipelines
    • Model Formats
    • AI As Data
    • MLOps Interview Questions
    • ML Architecture
  • DataOps
    • SQL
    • Tools
    • Databases
    • Database Modeling
    • Data Analytics
    • Data Engineering
    • Data Pipelines
    • Data Strategy
    • Data Vision
    • Data Teams
    • Data Catalogs
    • Data Governance
    • Data Quality
    • Data Observability
    • Data Program Management
    • Data KPIs
    • Data Mesh
    • Data Contract
    • Data Product
    • Data Engineering Questions & Training
    • Data Patterns
    • Data Architecture
    • Data Platforms
    • Data Lineage
  • DevOps
    • DevOps Strategy
    • DevOps Tools
      • Tutorials
      • Continuous Integration
      • Docker
      • Kubernetes
      • Cloud Objects
      • Key Value DB
      • API Gateway
      • Infrastructure As code
      • Logs
      • ELK
      • SLO
    • DevOps Courses
  • DevSecOps
    • Definitions
    • Tools
    • Concepts
  • Architecture
    • Problems
    • Development Concepts
    • System Design
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The Ops Compendium is your central hub for learning all things Ops—covering 80 topics across MLOps, DataOps, DevOps, DevSecOps, Architecture, and is continuously being updated. It’s similar to the Deep Learning & Machine Learning Compendium and is designed as an educational resource. Through it, I aim to help people learn and connect with the amazing authors whose work I’ve summarized, quoted, and referenced.

The Ops Compendium is a fully open project on GitHub (please star it!).

I am committed to education and knowledge sharing, ensuring that this compendium remains not-for-profit and freely accessible. I envision it as a go-to resource for individuals at all skill levels—industry professionals, data engineers, machine learning engineers, DevOps practitioners, data scientists, and academics alike. This compendium is designed to save you countless hours of searching and filtering through articles of uncertain value, while also connecting you with exceptional authors whose work you can further support.

Please note that this is an ongoing project covering a wide range of topics. If you think something needs improvement or updates, you can easily contribute via GitHub or reach out to me directly.

Many Thanks, Dr. Ori Cohen

My Website | Medium | LinkedIn | ML Compendium | State of GenAI | State Of MLOps |

LogoGitHub - orico/www.opscompendium.com: The Ops Compendium is a resource list for dataops, mlops, devops, etc, which I'm actively curating in order to expand my knowledge, it is now an open knowledge-sharing project compiled using Gitbook.GitHub
The Ops Compendium Official GitHub Repo