AI & Machine Learning
MLOps & AI Infrastructure
Deploy, monitor and manage ML models with MLflow, Docker and Kubernetes.
Overview
Learn how to take machine learning models to production and keep them healthy. You manage the model lifecycle with MLflow, package and serve models with Docker and Kubernetes, and monitor them on cloud infrastructure.
Course objectives
- Manage the ML lifecycle with MLflow
- Package and serve models with Docker and Kubernetes
- Automate ML with CI/CD
- Monitor models and detect drift
Who it's for
- DevOps and platform engineers supporting AI teams
- ML engineers deploying models to production
- Linux and cloud admins moving into AI infrastructure
Prerequisites
- Linux command-line skills
- Basic Docker knowledge
- Python and ML basics are helpful
Syllabus outline
- Why ML in production is different
- The MLOps lifecycle
- Roles and team workflows
Lab and practice approach
You run a full MLOps pipeline in your own lab — track, package, deploy and monitor a model on Kubernetes — as the course capstone.
Why attend this course?
- Bridges DevOps and AI — a rare, valuable mix
- Builds on Linux, container and Kubernetes skills
- Covers cloud AI infrastructure
After completing this course
Roles you can aim for
- MLOps Engineer
- AI Infrastructure Engineer
- Platform Engineer (AI)
What you will be able to do
- Model deployment on Kubernetes
- ML pipelines and CI/CD
- Model monitoring and observability
Assessments and capstone project
No vendor exam is required. You are assessed through assignments, quizzes and knowledge checks, technical assessments and a final capstone project.
- Assessment
- Assignments, quizzes and technical assessments
- Final project
- Capstone project reviewed by your instructor
- Vendor exam
- Not required
Why learn with OpenSprig
- Live, instructor-led online sessions you can ask questions in
- Practice on real systems in every class
- Exam-objective-aligned syllabus with mock tests
- Course community group for help between classes
- Weekend-friendly 2–3 hour classes
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