DevOps Engineer → MLOps Engineer

DevOps to MLOps Engineer: The 3-5 Month Transition Roadmap

A strategic roadmap for DevOps engineers transitioning to MLOps engineering. Your CI/CD, infrastructure, and automation skills transfer directly, you just need to learn what makes machine learning systems different. This path bridges your DevOps expertise with ML-specific requirements.

3-5 months

Difficulty: Intermediate

Prerequisites

Strong CI/CD pipeline experience (Jenkins, GitLab CI, GitHub Actions)
Infrastructure as Code proficiency (Terraform, Ansible, CloudFormation)
Container orchestration expertise (Docker, Kubernetes)
Monitoring and observability experience (Prometheus, Grafana, ELK)
Cloud platform experience (AWS, Azure, or GCP)
Scripting proficiency (Python or Bash)

Your Learning Path

ML Fundamentals for DevOps Engineers

3-4 weeks

Skills You'll Build

Machine learning pipeline conceptsModel lifecycle (training, validation, deployment)Data versioning and lineageUnderstanding model artifacts vs. code artifactsPython for ML (NumPy, Pandas, scikit-learn basics)

ML Pipeline Orchestration

3-4 weeks

Skills You'll Build

ML pipeline design patternsData pipelines vs. training pipelinesFeature store conceptsExperiment tracking (MLflow, Weights & Biases)Orchestration tools (Kubeflow, Airflow, Prefect)

Model Serving and Deployment

3-4 weeks

Skills You'll Build

Model serving patterns (batch vs. real-time)Model containerization and optimizationInference APIs with FastAPI/FlaskGPU resource managementA/B testing and canary deployments for models

Experiment Tracking and Model Registry

2-3 weeks

Skills You'll Build

Experiment logging and comparisonModel versioning and registry managementModel metadata and artifact storageReproducibility patternsCollaboration workflows for ML teams

ML Monitoring and Observability

2-3 weeks

Skills You'll Build

Model performance monitoringData drift and concept drift detectionPrediction quality metricsAlerting for ML systemsDebugging production ML issues

Portfolio and Career Transition

2-3 weeks

Skills You'll Build

Building end-to-end ML pipeline portfolioShowcasing DevOps → MLOps transitionMLOps engineer interview preparationPositioning infrastructure experience for ML roles

DevOps to MLOps Engineer: The 3-5 Month Transition Roadmap

Prerequisites

Your Learning Path

ML Fundamentals for DevOps Engineers

🎁 The AI Engineer Starter Kit

ML Pipeline Orchestration

Model Serving and Deployment

Experiment Tracking and Model Registry

ML Monitoring and Observability

Portfolio and Career Transition

🎁 The AI Engineer Starter Kit

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