Automated Pipeline on AWS — SageMaker MLOps & CI/CD

Tuxpas helps companies move Machine Learning models from manual, ad-hoc scripts and notebooks to a fully productive, automated and observable MLOps pipeline on AWS. We take your existing model, recommendation engines, forecasting, propensity, churn, scoring or any production-grade ML use case, and industrialize it using Amazon SageMaker Pipelines as the orchestration core, with SageMaker Model Registry for versioning and rollback, SageMaker Experiments for tracking, and SageMaker Model Monitor for drift detection.

The solution is delivered in three incremental levels. Level 1 (Automation): the pipeline runs autonomously on a schedule via Amazon EventBridge, with credentials managed in AWS Secrets Manager, containerized images in Amazon ECR, observability through Amazon CloudWatch and Amazon SNS, and connectivity to on-premise databases via Site-to-Site VPN. Level 2 (Model Governance): training and inference pipelines are separated, CI/CD is implemented with AWS CodePipeline + CodeBuild (or GitHub Actions), and an operational dashboard exposes execution status and model metrics. Level 3 (Autonomous Operation): automatic retraining on drift, A/B testing, feedback loop comparing model predictions vs real business outcomes, AWS CloudTrail auditing, and full handover with documentation and training.

Outcomes: zero manual intervention, secrets-free codebase, drift-triggered retraining without human input, and parity (≥95%) with the legacy system from day one. Ideal for any company running ML models in production, including CPG, retail, financial services, manufacturing, healthcare and B2B distribution.