How mpathic built better ML workflows by switching from Elasticsearch to ClickHouse Cloud

mpathic, a startup founded in 2021, aims to enhance patient safety and compliance in drug trials by leveraging AI to analyze therapy sessions, identifying issues before they impact the trials. Initially using Elasticsearch to manage its machine learning (ML) pipelines, the company faced challenges with inefficiencies and the inability to perform complex data operations, which hampered innovation and increased costs. Transitioning to ClickHouse improved mpathic's data processing capabilities significantly, allowing for faster and more efficient ML workflows by enabling in-database tasks that previously required additional compute resources. This switch not only accelerated data processing and reduced infrastructure management burdens but also enhanced data analysis, enabling the team to focus more on refining their ML models and less on system administration. As mpathic continues to evolve, it plans to expand its data analysis capabilities, using ClickHouse to further improve clinical trial outcomes and maintain model accuracy as therapy data patterns change.