Case Study: Sleepme achieves real-time bed-temperature optimization with Provectus managed AI services

Sleepme delivers real-time bed-temperature ML in 4 weeks with Provectus

Sleepme, a sleep-management and monitoring company, needed to move a proof-of-concept machine learning model for real-time bed-temperature control into a production-grade service. Their challenge was to build this scalable and observable ML infrastructure without diverting their own engineering team from core product development. They partnered with Provectus to implement a solution using Amazon SageMaker.

Provectus built and delivered a complete production ML pipeline on Amazon SageMaker in just four weeks, including CI/CD and managed MLOps. This solution provides real-time temperature recommendations to Sleepme's customers and is operated under Provectus's Managed AI Services. As a result, Sleepme's engineering team was able to focus on product innovation while achieving more stable production workloads and eliminating the operational overhead of infrastructure management.

Sleepme