60,000+
homecare patients
50,000+
caregivers
85M+
care hours annually
Industry
The client’s data science team deployed models by editing live notebooks in the AWS console with no version control, no review process, and no way to roll back a failed change. A single idle endpoint ran undetected for two months, incurring thousands of dollars in unnecessary charges. Inferenz built a production-grade ML platform on AWS SageMaker Unified Studio that enforced software engineering discipline, automated deployment pipelines, and gave leadership full visibility into every model change. Deployment time and production incidents dropped, and the team shifted from firefighting to innovation.
The data science team had domain expertise but no software engineering discipline. Models went directly from notebook to production with no checkpoints, and the consequences were financial, operational, and compliance-related.
Models were deployed by directly editing live notebooks in the AWS console. When a model failed mid-run, recovery meant manually searching S3 temp folders for a prior version, if one existed. There was no rollback, no history, and no process.
Model endpoints were deployed and forgotten. One ran unused for two months, incurring over $4,000 in charges before an Inferenz engineer spotted it in a routine cost review. The client’s manager had no knowledge it was running.
Third-party HIPAA auditors reviewed processes regularly. With no Git history, no PR records, and no deployment logs, auditors found documentation gaps across the data science team. Non-compliance carried the risk of regulatory penaltes on every audit.
Two years earlier, theclient had deployed SageMaker Studio (Legacy) with written process guidelines. Adoption was inconsistent. Data scientists kept working ad hoc. There were still no CI/CD pipelines, no enforced environments, and no validation before production changes.
Inferenz built a centralized ML platform on AWS SageMaker Unified Studio, chosen for its ability to unify data exploration and model development in a single environment.
The platform was built around one principle: every model going to production must go through the process. Development projects give data scientists freedom to experiment. Production projects enforce the rules, code in Git, reviewed via pull request, and approved before any deployment.
Version tags in GitHub create a deployment anchor at every release, enabling rollback to any prior version in minutes. Automated CI/CD pipelines replaced the two-day, engineer-dependent setup that had preceded every model deployment.
Endpoint lifecycle management was built to automatically retire idle endpoints, eliminating the cost leakage that had triggered the engagement. Every model change now carries a full audit trail — who proposed it, who reviewed it, who approved it, when it was promoted, giving HIPAA auditors structured, traceable records on every deployment.
SageMaker Unified Studio was a new platform when implementation began. AWS documentation was incomplete in places. The team resolved undocumented gaps independently and contributed corrections back to AWS through official support channels.
Deployment time
From 2 days per model to under 2 hours, for every model, every time, with no manual pipeline setup.
Fewer production incidents
Instant rollback via version tags replaced hours of manual S3 folder recovery when issues surfaced.
DS team productivity
Time recovered from incident firefighting redirected to new model development and innovation.
Idle endpoint cost
Automated endpoint lifecycle management eliminated the cost overrun class that originally triggered this engagement.
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