Certified MLOps Engineer

A 40-hour certification that turns trained models into reliable production services — packaging, deploying, scaling and monitoring ML with modern MLOps tooling. It bridges data science and operations so models keep delivering value after the notebook, through reproducible pipelines and automated delivery.

Working hands-on with Docker, Kubernetes, CI/CD and MLflow, you build the full delivery path for a model: containerize it, expose it as an API, automate its release, and watch it in production. Every module ends in practical labs, and the program closes with a capstone that deploys and monitors a live model.

• ML engineers and data scientists moving models into production.
• DevOps and backend engineers adding ML delivery to their skillset.

• Containerize and serve machine-learning models as production APIs.
• Build automated CI/CD pipelines for model delivery.
• Orchestrate and scale services with Docker and Kubernetes.
• Track experiments and manage model versions with MLflow.
• Monitor models in production and detect drift for retraining.

• Hands-on labs throughout the program (70% project-based).
• A capstone project demonstrating end-to-end skills.

• Package, deploy and serve models with Docker, Kubernetes and CI/CD.
• Track experiments and monitor models in production with MLflow.

Globally accredited certificate issued by Epsilon AI Learning — USA, with a unique Certificate ID and EPSILON ID. Awarded on 80% attendance, 80% final exam, and a capstone project.

• The MLOps lifecycle: from experiment to production
• Reproducibility, versioning and the cost of manual ML
• The modern MLOps toolchain at a glance

• Containerizing models with Docker
• Exposing models as REST APIs
• Batch vs. real-time serving patterns

• Kubernetes fundamentals for ML workloads
• Autoscaling and resource management
• Rolling updates and safe rollbacks

• Automated build, test and deployment pipelines
• Model validation gates before release
• Continuous delivery of models

• Tracking experiments with MLflow
• Model registry and stage promotion
• Comparing and reproducing runs

• Monitoring performance and data/concept drift
• Alerting and automated retraining triggers
• Capstone: deploy and monitor a production model