​

The rtemis ecosystem makes advanced machine learning accessible to all - scientists, clinicians, statisticians, computer scientists.

To do this, it provides:

• powerful, low-code APIs: high-level functions with complete controls of the entire data pipeline.
• no-code web applications: interactive, user-friendly interfaces.

It is actively used for:

• Machine learning algorithm development.
• Applied data science, with emphasis on biomedical basic research, clinical predictive modeling, and public health.
• Health Data Science education.

rtemis consists of 10 interconnected packages that work together to provide a complete ML ecosystem.

APIs ​

• rtemis: ML & visualization API in R.
• rtemisPy: ML & visualization API in Python.
• rtemis.jl: ML & visualization API in Julia.
• rtemisbio: Bioinformatics extension in R.
• kaimana-r: API access to state-of-the-art open source LLMs in R.
• kaimana-py: API access to state-of-the-art open source LLMs in Python.

Web applications ​

• rtemislive: Web application for ML & visualization.
• rtemisSeq: Interactive protein sequence visualization.
• rtemisXt: Interactive time-series data visualization.
• kaimana: AI Agent Chatbot using state-of-the-art open source LLMs.

Visit the Documentation links in the navigation bar to get started.