MLOps and AI
Deploy, monitor, and govern machine learning workloads on AWS with production-grade infrastructure.
Overview
We help teams move ML models from notebooks to production. Using SageMaker for training and deployment, Bedrock for foundation model access, and custom containerized endpoints when the workload demands it, we build infrastructure that scales with your data.
Every deployment includes monitoring, governance, and cost controls. We set up drift detection, automated retraining triggers, and budget guardrails so your models stay accurate without runaway spend.
Our methodology
Evaluate
We assess your data, models, and business requirements to determine the right deployment strategy. Not every problem needs a custom model.
Deploy
We build SageMaker pipelines, inference endpoints, or Bedrock integrations with automated scaling, versioning, and rollback controls.
Monitor
We set up model monitoring, drift detection, and cost alerting so your ML workloads stay accurate and affordable in production.
Deliverables
- Model training and deployment pipelines (SageMaker)
- Inference endpoints with auto-scaling and A/B testing
- Monitoring dashboards for model performance and drift
- Cost controls and budget alerts for ML compute
- Documentation for model lifecycle and governance processes