Reinventing Retail Forecasting with Autonomous MLOps on AWS

Background

Altimetrik Builds an Autonomous ML Ecosystem Driving Accuracy, Speed & Governance.

A global retailer partnered with Altimetrik to modernize its forecasting engine through a fully automated Champion/Challenger Machine Learning Framework powered by Amazon Sage Maker. The new MLOps ecosystem enables rapid experimentation, faster deployments, and zero downtime, reducing validation cycles from months to days while enhancing governance, observability, and cost efficiency.

The retailer’s legacy forecasting process relied on manual validation and limited transparency, slowing decisions across inventory, supply chain, and seasonal planning. As business complexity grew, the existing setup couldn’t support experimentation, enterprise governance, or real-time oversight, triggering the need for a modern, scalable MLOps foundation.

AI AWS Generative AI

Key Business Challenges

Slow & Manual Model Validation

Model evaluation required months of manual checks, limiting forecasting agility.

High Deployment Risk & Limited Experimentation

Introducing new algorithms was risky and lacked automated benchmarking against production models.

Insufficient Governance & Observability at Scale

Global operations needed stronger security, auditability, real-time monitoring, and cost visibility.

Solution Overview

Champion/Challenger ML Framework

• 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.

Operational Excellence & MLOps Automation

• 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.

Governance, Security & Compliance

• 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

Accelerated Innovation

Validation cycles reduced from months to days, enabling rapid experimentation.

Reliable, Continuous Forecasting

SageMaker ensures zero-downtime model

transitions with always-live Champion

models.

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Enterprise-Wide Efficiency & Governance

Improved accuracy, reduced operational overhead, enhanced security, and stronger cost optimization discipline.

Self-improving intelligence ecosystem

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.

Client Success Stories