MLOps Engineer Professional Certificate

Take machine learning models from a notebook to production: tracked experiments, automated pipelines, containers, serving and monitoring.

Learn the scripting, testing and CI/CD that production systems rely on, then track experiments and register models with MLflow, and learn the design patterns that keep ML systems maintainable. Package models in containers, run them on Kubernetes, serve them behind fast APIs and watch them for drift once they're live, and see how large organisations build data and ML platforms. Every course has hands-on labs and a capstone project your instructors review.

Prepares you for

MLOps engineer, Machine learning engineer, ML platform engineer, AI infrastructure engineer

8 courses in this Professional Certificate

  • Python Scripting and Linux for DevOps: Automate Linux servers with Python: scripts that manage files and run commands safely, command-line tools built with argparse and click, packages installed by script, and jobs scheduled with cron and systemd. (Beginner, about 16 hours)
  • CI/CD and Automated Testing: Build pipelines that test every change and release it safely: pytest suites with fixtures and coverage, GitHub Actions and Jenkins pipelines, quality gates, and deployment strategies that make releases routine. (Intermediate, about 17 hours)
  • MLOps Foundations: Experiments, Models and Reproducibility with MLflow: Track every experiment, package models with signatures, promote them with registry aliases and rebuild any training run, using MLflow on your own laptop. (Intermediate, about 16 hours)
  • Machine Learning Design Patterns: Solve the recurring problems of ML systems with proven patterns for representing data, framing problems, training, serving and reproducibility, and know what each one costs. (Intermediate, about 25 hours)
  • Containerisation and Orchestration with Docker and Kubernetes: Package Python services as lean, secure container images, run multi-container apps with Docker Compose, and deploy, scale and troubleshoot them on Kubernetes. (Advanced, about 19 hours)
  • Deploying and Serving Machine Learning Models: Serve models fast and safely: validated APIs, ONNX, gRPC and micro-batching, lean containers, canary and shadow releases, and load tests that tell the truth. (Advanced, about 17 hours)
  • Monitoring Models in Production: Drift, Data Quality and Alerts: Catch broken pipelines, measure drift with statistics you build yourself, track performance when labels arrive late, and design alerts and retraining policies people trust. (Advanced, about 17 hours)
  • Enterprise Data and ML Platforms: Architect data lakes, lakehouses, streaming pipelines, hybrid cloud deployments and governed MLOps for an enterprise, and build working miniatures of each on your laptop. (Advanced, about 26 hours)