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Sharif Ahamed@sharif485
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Machine Learning Engineer
United Kingdom
4 years of experience
About me
An independent introvert who likes to solve different real-world problems through technologies. From childhood, the words Why and How inspired me to accomplish my dream to be an Engineer. Currently, I am pursuing my MSc in Artificial Intelligence at the University of Bradford, UK. I am proficient in Python along with different ML algorithms on computer vision and natural language processing, TensorFlow, different database systems, and Docker
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š¤ Submissions
DoctorsMate
DoctorsMate incorporates advanced technological components like RAG (Retriever-Answer Generator) architecture, Cohere embeddings, Cohere Generate, and Weaviate as a vector database, each contributing to its functionality in unique ways. You as an acting doctor have to put together the symptoms, patient details, and the previous history(if any). Here the RAG architecture is central to the tool's ability to process and interpret medical information. It combines a retriever component by using the Cohere embeddings and the Cohere Generate endpoint to finalize the output at the end. Weaviate, as a vector database, plays a critical role in organizing and retrieving medical data efficiently. I have also used the Qdrant just for the log data collections (not related to the application use cases). Currently, It supports around 600 diseases.
