This repository contains instructions, template source code and examples on how to serve/deploy machine learning models using various frameworks and applications such as Docker, Flask, FastAPI, BentoML, Streamlit, MLflow and even code on how to deploy your machine learning model as an android app.

Explore the image embeddings of Unsplash using CLIP's image similarity

A Streamlit app for showing a TimelineJS about the history of Natural Language Processing

Predicta: Simplify your workflow with our powerful data analysis and machine learning tool.

A cookiecutter template for Streamlit app

A Streamlit App to test your general knowledge.

Drug Discovery Using AI for Breast Cancer

Our EduApp is specifically designed to address the educational challenges faced by rural students. It uses an Angular frontend with robust Express engine facilitating the Node.js framework and uses MongoDB for scalable data handling. To enhance the user experience, we've implemented machine learning techniques with the help of the Gemini Pro model

Infographs provides detailed analytics with the help of predictive algorithms and statistical modeling covering areas such as customer crunch, stock prediction with profit and loss statistics, analyzing your customer reviews, and generating keywords for further analysis.

CerviCare: Your Partner in Cervical Wellness, Every Step of the Way.

🐬 Web Design Template made with Streamlit 🐝

🔴 Pokemon web - Streamlit

Application de déploiement de modèle de machine learning basé sur le concept de segmentation du marché.

A web based ML application made with streamlit that performs some NLP tasks.

A small cv in streamlit

Diabetes Prediction: Using machine learning to classify individuals as diabetic or non-diabetic based on health data, enabling early intervention and improved healthcare outcomes.

Platform provides comprehensive healthcare solution.

Website to explain the working of Cyberbullying detection and prevention app in social media