Pinned Repositories
AG_News_Classification
End-to-end MLOps pipeline for AG News Classification using transformer models like RoBERTa, achieving 94.53% accuracy. Features FastAPI backend, Redis-Celery for task handling, AWS EC2 for deployment, CircleCI for CI/CD, and a user-friendly frontend for real-time predictions.
AI_Agents_Tutorial
Augmentor
Automated_MCQ_Generator
Bank-Management-System-
Chest-Disease-Classification-Final-Version
An MLOps-powered pipeline for chest disease classification from CT scans using ResNet50, achieving 89.52% accuracy. Includes data versioning with DVC, experiment tracking with MLflow, automated model evaluation, and scalable deployment via Flask API and Docker.
E-Commerce_ChatBot
An advanced E-commerce chatbot using RAG, LLaMA 3.1-8B, and LangChain, deployed on AWS EC2. Features history-aware chat, product recommendations using HuggingFace embeddings stored in AstraDB, and efficient response handling with Redis-Celery. Responsive web interface for a seamless user experience.
Industry_Safety_Detection_Using_YOLOv8
An MLOps pipeline for real-time industrial safety detection using YOLOv8, achieving 88.90% mAP_50 accuracy. Automates data ingestion, validation, and evaluation with MLflow, and integrates CI/CD for deployment on AWS using Docker, ensuring scalability, efficient updates, and robust safety compliance.
Interview_Question_Creator
AI-powered tool for generating domain-specific interview questions and answers from PDFs. Leverages Mistral LLM via LangChain, FAISS for embeddings, Redis/Celery for async tasks, and FastAPI backend. Deploys on AWS EC2 for scalability, providing tailored questions across technical, theoretical, and behavioral domains.
phishing-classifier
A machine learning-powered phishing URL detection system built with Python and Flask, featuring MLOps practices for automated training, evaluation, and deployment to ensure scalability and reliability in combating cyber threats.
jatin-12-2002's Repositories
jatin-12-2002/sensor-fault-project
Problem Statement The Air Pressure System (APS) is a critical component of a heavy-duty vehicle that uses compressed air to force a piston to provide pressure to the brake pads, slowing the vehicle down. The benefits of using an APS instead of a hydraulic system are the easy availability and long-term sustainability of natural air.
jatin-12-2002/mlflow-intro
jatin-12-2002/Project
jatin-12-2002/pypi_templat
jatin-12-2002/exmp_pypi
jatin-12-2002/practice72
jatin-12-2002/waferfault63
jatin-12-2002/waferfault67
jatin-12-2002/waferfault
jatin-12-2002/Practice
jatin-12-2002/reviewscraper
This is my deployed app
jatin-12-2002/seaborn-data
Data repository for seaborn examples
jatin-12-2002/T-test-an-Correlation-using-python