The Business– Quin is a fast-growing start-up in the healthcare sector. Their aim is to create objectivity and transparency around the usefulness and necessity of medical treatments. Quin wants to be an addition to the current system of healthcare. They aim to do this by supporting patients, doctors and medical specialists to make the right decisions based on large amounts of historical data. Quin uses AI to provide accessible, personalized and evidence-based advice regarding the patient’s care path.
The Challenge– To be able to work with large amounts of personal data, it is key to create a solid and highly secured data infrastructure. This infrastructure needs to serve as the fundament for various digital platforms and AI applications. It has to be designed in a way that makes the continuous improvement of data(quality) and models possible. In other words, the solutions built have to be scalable and future-proof.
The Solution – Enjins designed and implemented a data-infrastructure which is highly secured, client centric and allows for direct and two-way communication. The structure enables the Quin data science team to train and deploy their own Machine Learning models and integrate new services in the future.
The Technology– To realize a scalable and future-proof data science infrastructure, Enjins leverages the power of open-source software and technologies. Among these are the highly-popular Kubernetes, to allow for a scalable infrastructure, Docker, to enable containerized deployment of applications, and finally Jupyter, to allow Data Scientists to collaborate on projects within this secure environment.