AI Systems & MLOps

Take AI from notebook to production — and keep it there.

Docker, cloud deployment, CI/CD, and model monitoring.

What You'll Learn

Structure ML projects for reproducibility and collaboration

Version data and models with DVC and Git

Track experiments with MLflow and Weights & Biases

Containerise ML services with Docker

Deploy models to AWS SageMaker and GCP Vertex AI

Build CI/CD pipelines for automated model retraining

Monitor model performance and detect data/model drift

Course Curriculum

Week 1–2ML Project Structure & Versioning

Cookiecutter project templates
Git for ML: branching strategies
DVC for data and model versioning
Reproducible experiments with config files

Week 3–4Experiment Tracking

MLflow: logging metrics, params, artifacts
Model registry and stage transitions
Weights & Biases dashboards
Comparing experiments and selecting winners

Week 5–6Containerisation & APIs

Docker basics for ML engineers
Packaging ML models in Docker containers
FastAPI for model serving
Docker Compose for multi-service stacks

Week 7–9Cloud Deployment

AWS SageMaker: training jobs and endpoints
GCP Vertex AI: pipelines and model registry
Auto-scaling and load balancing
Cost optimisation strategies

Week 10–12CI/CD & Monitoring

GitHub Actions for ML pipelines
Automated model retraining triggers
Model monitoring: drift detection with Evidently
A/B testing model versions in production

Week 13–16Capstone & Portfolio

End-to-end MLOps pipeline for a real use case
Infrastructure as code with Terraform basics
Responsible AI governance checklist
Career prep: portfolio, resume, and mock interviews

Projects You'll Build

Automated model retraining pipelineReal-time fraud detection service (end-to-end)A/B tested recommendation APIMLOps capstone: deploy a model to production with full monitoring

Tools & Technologies

PythonDVCMLflowWeights & BiasesDockerAWS SageMakerGCP Vertex AIGitHub ActionsFastAPIEvidentlyTerraform (basics)

Prerequisites

▸Machine Learning Essentials or Deep Learning
▸Python proficiency
▸Basic Linux command line and Git

Who It's For

▸College final-year students (CS/Data Science)
▸ML engineers wanting to scale
▸Anyone targeting senior AI engineering or MLOps roles

Enroll Now

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AI Systems & MLOps

16 weeks

Duration

Advanced

Level

Yes

Certificate

Online / Offline

Mode