Cloud Computing: AWS, GCP, Azure, and Databricks
A cloud learning path for teams working with data engineering, ML infrastructure, and scalable analytics delivery across AWS, GCP, Azure, and Databricks.
Cloud
Local to Multi-Node: Packaging ML Code with Docker
How to build lean Docker images for ML workloads without carrying notebook-era assumptions into production clusters.
Setting Up a Clean CI/CD Pipeline for Python ML Packages
How to structure testing, packaging, and trusted publishing for Python ML libraries without turning your release workflow into a bottleneck.
Machine Learning Engineering Bootcamp
Programme Week Topic 1–4 Python, statistics, data wrangling 5–8 Supervised learning, model evaluation 9–12 Deep learning, NLP, computer vision 13–16 MLOps, GCP deployment, capstone Materials Lecture notes and notebooks are available to enrolled students via the course portal.