Reliable & Scalable AI Deployment

I deploy machine learning solutions using modern tools and platforms such as FastAPI, AWS, and Docker. From APIs to cloud integration, I ensure seamless and production-ready deployment of data-driven systems.

From Local Models to Production Systems with MLOps

I've led end-to-end deployment pipelines involving MLflow, DVC, GitHub Actions, and Kubernetes. Whether it's automating CI/CD workflows or integrating monitoring tools like Prometheus and Grafana, I ensure smooth operationalization of ML models.

• FastAPI-based REST APIs tested and containerized with Docker.
• Deployed applications on AWS with Kubernetes for auto-scaling and monitoring.
• Integrated MLflow & DVC for experiment tracking and reproducibility.

I follow best practices in MLOps to ensure deployments are repeatable, trackable, and scalable. My systems are designed to serve models in real-time with minimum latency and maximum uptime.

Whether it's a recommendation engine or an AI-powered medical assistant, I bring models from notebooks to users — securely, efficiently, and reliably.