MLOps & Microservice Productionisation
Notebook-to-Production Pipeline
Productionisation of 15+ machine learning models into containerized FastAPI microservices with Airflow on AWS.
The Engineering Challenge
Data science teams had accumulated unmaintained Jupyter notebooks lacking standardized APIs, monitoring, and automated retraining.
Core ProblemBridge the gap between experimental notebook prototypes and highly available production services with SLA guarantees.
- Sub-100 ms API latency budgets
- Automated model drift detection and retraining
System Architecture
End-to-end data pipelines and component isolation boundaries.
Dockerized FastAPI microservice architecture managed with Apache Airflow DAGs, automated GitHub Actions CI/CD, and AWS deployment.
Key Engineering Decisions
Standardized Docker Base Templates
Created minimal, hardened CUDA/CPU container images cutting deployment build times by 60%.
Production Verification & Results
- Shipped 15+ disparate models into live AWS production microservices
- Maintained 99.9% uptime with automated health checks and blue-green deployments
Production Stack & Tooling
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I specialize in taking complex agentic workflows, duplex voice architectures, and high-throughput computer vision models into production.