// MLOps / Azure
An end-to-end MLOps pipeline on Azure
ML models were trained and shipped by hand — no reproducibility, slow promotion. I built a fully automated Azure ML + Azure DevOps pipeline covering training, validation, registration, and deployment to managed endpoints.
Impact Metrics
100%
Model lifecycle automated
0
Manual deploy steps
Self-serve
Infra provisioning
Stack Applied
// Context & Background
An enterprise AI team was moving real machine-learning workloads to production, but data scientists handed off models manually. There was no reproducible path from a trained model to a live endpoint, and promoting between environments was slow and error-prone.
// The Challenge
Make model training and deployment reproducible, governed, and self-service across environments — without forcing the data-science team to become infrastructure engineers.
// The Roadmap & Approach
Azure ML lifecycle automation
Designed end-to-end workflows on Azure Machine Learning to automate training, evaluation, registration, and deployment to real-time and batch inference endpoints.
Infrastructure as code with Terraform
Modular Terraform provisioned ML workspaces, Synapse, networking, and storage — with a Hub-and-Spoke architecture enforcing secure isolation across AI workloads.
Azure DevOps CI/CD pipelines
Parameterized YAML pipelines provision infrastructure, train models, and deploy endpoints — with approval gates, environment promotion, and rollback built in.
Self-service accelerator platform
A full-stack accelerator (Python backend, Next.js frontend) renders input forms from Terraform variables, letting teams provision compliant AI infrastructure through a web UI.
// Validated Results
- Models go from trained to deployed with zero manual steps
- Reproducible, approval-gated promotion across staging and production
- Reusable Terraform modules standardize every new ML environment
- Teams self-serve infrastructure instead of filing tickets
Facing similar architecture challenges?
Let's talk through your system architecture — a 30-minute review session usually surfaces the quickest reliability and FinOps improvements.