ML Workflow Automation & Model Deployment
Getty Images, USA · Digital Media
Digital Media
Getty Images, USA
Automated end-to-end machine learning workflows for computer-vision and search models — from dataset versioning through training, evaluation, and governed production deployment.
Key outcomes
- Automated retraining & deployment
- Benchmarked inference efficiency
Scope of work
MLOps architecture, CI/CD automation, and production inference optimization
Business challenge
Model delivery lacked a repeatable path from dataset version to production. Inference cost and latency were not benchmarked, and experiment metadata was scattered across teams, limiting reproducibility and auditability.
Our approach
Architected GitLab-driven SageMaker workflows with versioned datasets and hyperparameter tuning. Deployed TensorRT, Ray, and Triton inference on Docker and ECR, built EMR-to-SageMaker retraining pipelines, and established Aurora-backed metadata models with automated lineage tracking.
Technology stack
Capabilities delivered
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