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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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