Setting up my ML environment from scratch: MedMind
Akhilesh is building a clinical AI system called MedMind from scratch, focusing on understanding the underlying mechanics of machine learning models rather than relying on pre-built APIs. The project involves setting up a full ML pipeline including data processing, model training, retrieval, and deployment using Python 3.11 and various AI libraries. Initial setup includes creating a virtual environment, installing key packages, and organizing the project structure for scalability.
- ▪Akhilesh is building MedMind, a clinical decision support system, without using OpenAI's API.
- ▪The setup uses Python 3.11 for optimal compatibility with PyTorch and HuggingFace libraries.
- ▪Core libraries include transformers, peft, trl, chromadb, and FastAPI for building and serving the model.
- ▪The project is structured into distinct directories for data, training, retrieval, evaluation, API, and frontend components.
- ▪Google Colab with a free T4 GPU is being used for model training due to lack of local GPU resources.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 1358056) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Akhilesh Posted on May 2 Setting up my ML environment from scratch: MedMind #python #ai #productivity #beginners I decided to build a clinical AI system from scratch. Not using the OpenAI API. Not a tutorial project. An actual system where I train my own model, build my own search pipeline, and deploy the whole thing. This post is about Day 1 — setting up everything before writing a single line of ML code.
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