Case Study: Theator achieves cost savings and higher MLOps productivity with ClearML

Theator saves $130K-$170K annually with ClearML

Theator, a company developing a Surgical Intelligence platform using AI and computer vision, faced a significant challenge in managing the high costs and operational overhead of its cloud-based AI development. Their data science team required extensive GPU resources for model training, but manually managing these cloud machines to avoid wasteful spending was a major distraction. They needed a solution to automate this process without having to build their own tool or hire dedicated DevOps staff.

Theator implemented ClearML's open-source MLOps platform for automation and orchestration. The solution automatically spun cloud machines up and down based on demand, leading to an estimated annual savings of $130K-$170K. ClearML also provided critical visibility into machine resources and ensured effortless experiment reproducibility, which significantly boosted productivity and ensured compliance in their regulated industry. The vendor's agnostic deployment allowed Theator to maintain its workflows while seamlessly scaling for future growth.

Theator