Our AI model management platform allows for model development, testing, deployment, monitoring, and maintenance across the lifecycle of a model. This enables data science teams to rapidly bring AI-based insights into production workflows seamlessly.
Enterprise AI for Healthcare
We are built around AI. F1careAI automates the hard problems around health data integration and data preparation for AI. Our AI Platform does the hard work so that healthcare analytics teams can focus on enabling BI and AI-powered ROI generating insights.
Build & Train AI
Our AI Platform has a rich, clinically validated feature library with 100s of healthcare attributes. Auto-generated by the underlying data pipeline, these features are ready for AI and ML model usage and provide a quick and easy way to develop AI-based use-cases.
AI platform allows for training datasets that reference the ingested data pipeline as well as public data sets. Training data sets are available in our AI model development workspace and allows sharing, monitoring, and tracking during model development and testing.
Our AI platform provides research-validated Phenotypes that enable segmentation of underlying data into common use-case applications, accelerates variation analysis, and enables agile experimentation & usage in model development, training, and testing.
Our AI model engine allows stateless model scoring. This enables the hosting of models developed and trained on other data sets to be scored on our managed data pipeline. This helps in bringing in-house projects and experiments to production fostering innovation.
With the capability to support real-time data feeds such as HL7 and IOMT data, our data pipeline aggregates streaming and batch data into an ML pipeline for model development, testing, and deployment. This enables a wide variety of real-time data use-cases.
BI to AI.
F1careAI platform for digital health is at the core of a healthcare organization’s journey from BI to AI to ROI. F1careAI platform brings a rich set of AI development features that allow for experimental model development as well as testing and production management of in-house developed models. Healthcare organizations can adopt AI within their workflows at their own pace.