Become an MLOps Engineer
MLOps Practitioner Journey
Operationalize Machine Learning & AI Models with Confidence. Build Production-Ready AI Pipelines, Master Cloud & Kubernetes MLOps, and Earn Your Minidegree Certification.
2 Courses
in this Journey
Advanced Level
Only for existing Devops Professionals
8+
Missions/Projects
3 months
at 5 hours a week
Flexible Schedule
Learn at your own pace
Why MLOps Matters ?
Machine Learning is no longer confined to research labs. Every modern business depends on AI-powered applications — but building models is only the beginning.
The real challenge? Operationalizing those models in production reliably, cost-effectively, and at scale.
That’s where MLOps & AI Platform Engineering come in.
You need MLOps to
- Bridge the gap between Data Science, Software Engineering & DevOps
- Automate model training, deployment & monitoring pipelines
- Work across Kubernetes, Cloud Platforms & LLM Applications
Introducing the MLOps Practitioner Journey
The MLOps Career Track/ Minidegree from School of DevOps is your complete roadmap to mastering ->
Whether you’re an ML Engineer, Software Developer, or DevOps Engineer, this structured learning path will help you build, deploy, monitor & scale machine learning and AI applications — across open-source and cloud platforms.
Your roadmap to be an expert at
- MLOps (Machine Learning Operations)
- Model Packaging with Containers
- Model Serving and Inference on Kubernetes
- Model Monitoring and Scaling
📚 What You’ll Learn
✅ Build end-to-end MLOps pipelines
✅ Automate model training & deployment with CI/CD
✅ Deploy ML & LLM models to Kubernetes & cloud platforms
✅ Monitor models with Prometheus, Grafana & EvidentlyAI
✅ Build secure, compliant & scalable AI platforms
MLOps Practitioner
Learn how to Package, Server and Scale a ML Model as well as setup Automated Pipeline with Data Engineering, Feature Engineering, Training, Deployment and Retraining Pipelines.
MLOps Practitioner - Journey
Course – Foundations of ML, GenAI And Agentic AI
1 AI Trinity CreditApprox 12 Hours of Learning Content
What you'll learn
- Essentials of Machine Learning
- Introduction to Machine Learning Algorithms
- Introduction to GenAI and LLMs
- Introduction to Agentic AI
Check out this course at https://schoolofdevops.com/programs/mastering-python-for-ai-ml/
Course – MLOps Bootcamp
2 AI Trinity CreditApprox 45 Hours of Learning Content
What you'll learn
- Build end-to-end Machine Learning pipelines with MLOps best practices
- Understand and implement ML lifecycle from data engineering to model deployment
- Set up MLFlow for experiment tracking and model versioning
- Package and serve models using FastAPI and Docker
- Automate workflows using GitHub Actions for CI pipelines
- Deploy inference infrastructure on Kubernetes using KIND
- Use Streamlit for building lightweight ML web interfaces
- Learn GitOps-based CD pipelines using ArgoCD
- Learn GitOps-based CD pipelines using ArgoCD
- Serve models in production using Seldon Core
- Monitor models with Prometheus and Grafana for production insights
- Understand handoff workflows between Data Science, ML Engineering, and DevOps
- Build foundational skills to transition from DevOps to MLOps roles
Check out this course at https://schoolofdevops.com/programs/mlops-bootcamp
MLOps Minidegree
School of Devops Certified MLOps Practitioner is awarded to the learners who have shown capability
