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Curriculum plan

DevOps, Kubernetes & MLOps — Skills Plan

Planned skill map for DevOps, Kubernetes & MLOps. The curriculum will be developed and released in stages.

26 planned skills October 2026
Dedicated track in progress. This map is a plan, not a claim that every lesson is published. Existing live learning tracks remain available while this path is developed.

Package and release applications with CI/CD, containers, Kubernetes, and infrastructure as code, then extend the workflow to model serving and MLOps.

Potential roles: DevOps Engineer · Platform Engineer · MLOps Engineer

Before you start

  • Linux command line
  • Networking and HTTP
  • Git and basic scripting
  • Application and Python fundamentals

What you’ll build

  • Containerized CI/CD service
  • Kubernetes deployment with monitoring
  • Reproducible ML deployment pipeline

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26skills mapped
6learning stages
0published lessons

Prerequisites

PLANNED
  • Linux command line

    Build the Linux command line knowledge needed to follow the DevOps, Kubernetes & MLOps foundations.

  • Networking and HTTP

    Build the Networking and HTTP knowledge needed to follow the DevOps, Kubernetes & MLOps foundations.

  • Git and basic scripting

    Build the Git and basic scripting knowledge needed to follow the DevOps, Kubernetes & MLOps foundations.

  • Application and Python fundamentals

    Build the Application and Python fundamentals knowledge needed to follow the DevOps, Kubernetes & MLOps foundations.

Foundations

PLANNED
  • Shell scripting

    Explain how Shell scripting works and where it fits in the DevOps, Kubernetes & MLOps learning path.

  • Containers and Docker

    Explain how Containers and Docker works and where it fits in the DevOps, Kubernetes & MLOps learning path.

  • CI/CD principles

    Explain how CI/CD principles works and where it fits in the DevOps, Kubernetes & MLOps learning path.

  • Cloud identity and networking

    Explain how Cloud identity and networking works and where it fits in the DevOps, Kubernetes & MLOps learning path.

Core Skills

PLANNED
  • Kubernetes workloads and services

    Use Kubernetes workloads and services to implement a working capability and verify its behavior in DevOps, Kubernetes & MLOps.

  • Helm and configuration

    Use Helm and configuration to implement a working capability and verify its behavior in DevOps, Kubernetes & MLOps.

  • Infrastructure as Code with Terraform

    Use Infrastructure as Code with Terraform to implement a working capability and verify its behavior in DevOps, Kubernetes & MLOps.

  • Secrets and environment management

    Use Secrets and environment management to implement a working capability and verify its behavior in DevOps, Kubernetes & MLOps.

  • Build, test, and release pipelines

    Use Build, test, and release pipelines to implement a working capability and verify its behavior in DevOps, Kubernetes & MLOps.

Advanced / Production

PLANNED
  • GitOps and deployment strategies

    Evaluate GitOps and deployment strategies trade-offs against the performance, reliability, and safety needs of DevOps, Kubernetes & MLOps systems.

  • Monitoring, logs, traces, and alerts

    Evaluate Monitoring, logs, traces, and alerts trade-offs against the performance, reliability, and safety needs of DevOps, Kubernetes & MLOps systems.

  • Reliability and incident response

    Evaluate Reliability and incident response trade-offs against the performance, reliability, and safety needs of DevOps, Kubernetes & MLOps systems.

  • MLflow and model registries

    Evaluate MLflow and model registries trade-offs against the performance, reliability, and safety needs of DevOps, Kubernetes & MLOps systems.

  • Model serving and data/model versioning

    Evaluate Model serving and data/model versioning trade-offs against the performance, reliability, and safety needs of DevOps, Kubernetes & MLOps systems.

  • MLOps evaluation and drift monitoring

    Evaluate MLOps evaluation and drift monitoring trade-offs against the performance, reliability, and safety needs of DevOps, Kubernetes & MLOps systems.

Real-World Projects

PLANNED
  • Containerized CI/CD service

    Deliver a working outcome with Containerized CI/CD service, then test it against the project requirements.

  • Kubernetes deployment with monitoring

    Deliver a working outcome with Kubernetes deployment with monitoring, then test it against the project requirements.

  • Reproducible ML deployment pipeline

    Deliver a working outcome with Reproducible ML deployment pipeline, then test it against the project requirements.

Career / Interview Preparation

PLANNED
  • Infrastructure and deployment design

    Explain your approach to Infrastructure and deployment design, defend key trade-offs, and discuss what you learned.

  • Kubernetes troubleshooting

    Explain your approach to Kubernetes troubleshooting, defend key trade-offs, and discuss what you learned.

  • Reliability scenarios

    Explain your approach to Reliability scenarios, defend key trade-offs, and discuss what you learned.

  • MLOps architecture review

    Explain your approach to MLOps architecture review, defend key trade-offs, and discuss what you learned.

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