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Reinventing Retail Forecasting with Autonomous MLOps on AWS
Altimetrik Builds an Autonomous ML EcosystemDriving Accuracy, Speed & Governance
A global retailer partnered with Altimetrik tomodernize its forecasting engine through afully automated Champion/Challenger MachineLearning Framework powered by AmazonSageMaker. The new MLOps ecosystem enablesrapid experimentation, faster deployments, andzero downtime, reducing validation cycles frommonths to days while enhancing governance,observability, and cost efficiency.
The retailer’s legacy forecasting process reliedon manual validation and limited transparency,slowing decisions across inventory, supply chain,and seasonal planning. As business complexitygrew, the existing setup couldn’t supportexperimentation, enterprise governance, orreal-time oversight, triggering the need for amodern, scalable MLOps foundation.
Key Business Challenges
Model evaluation required months of manual checks, limiting forecasting agility.
Introducing new algorithms was risky and lacked automated benchmarking against production models.
Global operations needed stronger security, auditability, real-time monitoring, and cost visibility.
Solution Overview
• SageMaker manages training, tuning, drift detection, and production orchestration.
• Challenger models are benchmarked against Champion using identical data.
• Only outperforming models progress to production, ensuring continuous improvement without downtime.
• GitLab CI/CD pipelines for automated testing, security scans, and deployments.
• Datadog, Splunk, and PagerDuty for performance monitoring, logging, and incident response.
• Terraform provisioning ensures consistent, repeatable, cloud-native infrastructure.
• IAM, KMS encryption, MFA, VPC isolation, and Azure SSO integration.
• Full auditability via CloudTrail and compliant enterprise-grade architecture.
• Cost governance through AWS CUR insights and Cloudability.
Business Outcomes
Validation cycles reduced from months to days, enabling rapid experimentation.
SageMaker ensures zero-downtime model
transitions with always-live Champion
models.
Improved accuracy, reduced operational overhead, enhanced security, and stronger cost optimization discipline.
Altimetrik transformed the retailer’s forecasting into a self-improving intelligence ecosystem. The automated Champion/Challenger MLOps framework delivers continuous innovation, stronger governance, and business-ready reliability, creating a scalable blueprint for AI-driven decision-making in modern retail.
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