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Cloud-Native MLOps (Amazon SageMaker)
We build scalable, secure MLOps pipelines using SageMaker - enabling rapid experimentation, deployment, monitoring, and governance of machine learning models.
Data Platform & MLOps
SageMaker provides a rich ecosystem for end-to-end ML lifecycle management. We help clients use it to streamline everything from data prep and training to model deployment and monitoring. With built-in CI/CD, lineage, and auto-scaling, your models go from lab to production with agility and control.
What we can do with it:
Build training and inference pipelines in SageMaker.
Enable distributed training and hyperparameter tuning.
Deploy models using multi-model endpoints or SageMaker Studio.
Track lineage, versions, and performance metrics.
Automate deployment with GitOps-style MLOps workflows.
Integrate SageMaker with event-driven and real-time systems.
Secure model endpoints with VPC and encryption settings.
Monitor drift and retrain triggers with SageMaker Model Monitor.
Use SageMaker Clarify for explainability and bias detection.
Support hybrid workflows with SageMaker + on-prem training.