I build production-grade deployment and monitoring infrastructure for AI models so your model doesn't just work in a notebook, it runs reliably in the real world with automated checks and alerts.
What's included:
Dockerized deployment with Docker Compose for consistent environments
Apache Airflow DAGs for automated scheduled inference pipelines
PostgreSQL integration for storing predictions and model state
Automated data quality validation (Great Expectations)
Real-time monitoring dashboards and alerting
FastAPI endpoint for serving model predictions
Ideal for: companies that have a trained ML model but need it properly deployed, automated, and monitored in production.
I build production-grade deployment and monitoring infrastructure for AI models so your model doesn't just work in a notebook, it runs reliably in the real world with automated checks and alerts.
What's included:
Dockerized deployment with Docker Compose for consistent environments
Apache Airflow DAGs for automated scheduled inference pipelines
PostgreSQL integration for storing predictions and model state
Automated data quality validation (Great Expectations)
Real-time monitoring dashboards and alerting
FastAPI endpoint for serving model predictions
Ideal for: companies that have a trained ML model but need it properly deployed, automated, and monitored in production.