Project overview
An American multinational retail company had built multiple ML models for demand forecasting, customer behavior analysis and dynamic pricing — but had no standardized way to deploy, monitor or retrain them in production.
Challenges
- Lack of standardized pipelines to deploy and monitor ML models in production
- Manual handoff between data science and DevOps teams caused delays
- Difficulty retraining models with fresh data and scaling across business units
- No unified observability or governance for model performance in production
Our approach
We delivered a production-grade MLOps platform that bridged the gap between data science and operations.
Automated model deployment pipelines
- Built end-to-end CI/CD with GitLab CI and Terraform
- Automated model packaging, testing and deployment to SageMaker endpoints and EKS APIs
- Managed SageMaker instances, artifacts and endpoint configuration as code
Feature store & data management
- Centralized feature store on Amazon S3 with AWS Glue Catalog
- Standardized feature engineering, lineage, versioning and reproducibility
Monitoring & retraining
- CloudWatch and Lambda for real-time performance and data drift alerts
- SageMaker Model Monitor for bias, latency and stale-data detection
- AWS Step Functions retraining workflows with canary and blue/green rollouts
Outcomes
- Reduced ML model deployment time from weeks to under 2 hours
- Decreased model failure rate in production by 65%
- Enabled self-service model deployment for data scientists
- Established a single MLOps platform with auditable, reproducible processes