What I led
Led architecture and hands-on implementation for model packaging, deployment, autoscaling, endpoint operations, and versioning workflows.
SageMaker Inference Platform & MLOps
Built production-ready ML deployment pipelines for client environments requiring repeatable inference operations, autoscaling, and versioned model lifecycle management.
Stack
AWS SageMakerAWS API Gateway + Lambda integration for endpoint access/automationAWS CloudWatchHistorical training-pipeline workflowsStep FunctionsCI/CD and infrastructure-as-code workflows+1 more
Highlights
- Built production deployment workflows for text-classification and related ML models.
- Implemented inference image build and deployment pipelines.
- Published and operated autoscaled endpoint services in AWS using multi-model inference image approaches where appropriate.
- Established model versioning and release patterns for ongoing model lifecycle updates.
- Built custom extraction pipelines (including AWS Textract-driven flows) that convert ingested files to markdown for downstream indexing and model workflows.
- Implemented GPU acceleration for legacy FastText-based endpoints via custom inference images to improve throughput.
- Extended pipeline support for emerging generative-AI extraction demos and prompting queues (pre-production).
Outcomes
- Enabled sustained production operation of approximately 10 active ML endpoints in client AWS environments.
- Improved delivery reliability and operational readiness of model-serving workflows.
- Top endpoint serves roughly 200 requests per week in a high-importance workflow used by an app with around 1,000 users.
- Achieved approximately 20% inference performance improvement on selected workloads through GPU-enabled custom image enhancements.
- Endpoint availability has remained near 100%, with downtime primarily tied to upstream cloud-provider outages.