Description
MLOps & AI Deployment ADVANCE COURSE
Building a machine learning model is one challenge. Deploying it reliably into production — and keeping it reliable — is another.
MLOps & AI Deployment is an expert-level technical course that develops the engineering knowledge required to build, deploy, monitor and maintain machine learning systems in real production environments.
The course follows the complete MLOps lifecycle and bridges the gap between experimental machine learning and operational AI.
Across six modules and 36 lessons, you will explore the systems, tools and engineering practices used to create reproducible, automated and maintainable ML environments.
Begin with the foundations of MLOps and examine why successful notebook-based models frequently encounter problems when moved into production. Explore MLOps maturity models, versioning practices and the modern MLOps technology landscape.
Develop professional experiment-management skills using MLflow and Weights & Biases. Learn how model registries, reproducibility practices and hyperparameter optimisation help transform experimentation into a controlled production workflow.
Build modern data pipelines using technologies including DVC, Feast, Tecton, Great Expectations, Apache Airflow, Prefect, Dagster and Kafka.
Move into production-grade CI/CD for machine learning, where you will examine automated code, data and model testing, GitHub Actions, GitLab CI, Docker containerisation, FastAPI model serving and controlled deployment strategies including blue-green and canary releases.
Explore cloud deployment across AWS SageMaker, Google Vertex AI and Azure Machine Learning and learn how Kubernetes can provide scalable, platform-independent model-serving infrastructure.
Finally, learn how production ML systems are monitored and maintained. Examine data drift, model performance degradation, observability, logging, tracing, alerting and automated retraining.
The course also introduces the governance practices increasingly required for responsible production AI, including model cards, audit trails and fairness monitoring.
A final capstone brings these capabilities together through the development of a complete MLOps pipeline from training to monitored production deployment.
This course is designed for ML engineers, data scientists moving into production roles, DevOps engineers working with AI systems and platform engineers building ML infrastructure.
It is an advanced technical program rather than an introduction to AI. Learners should already have working knowledge of machine learning and Python.
If you are ready to move beyond developing models and learn how professional AI systems are actually deployed, operated and maintained, MLOps & AI Deployment provides the technical pathway from experimentation to production.
