Found 183 repositories(showing 30)
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HimadeepRagiri
A self-taught AI/ML Engineer and Data Scientist showcase of 30+ production-ready and research projects in Machine Learning, Deep Learning, NLP, and MLOps. Built end-to-end pipelines, real-time systems, and deployable apps using tools like Docker, Kubernetes, Airflow, MLflow, TensorFlow, PyTorch, Hugging Face, and cloud platforms (GCP, AWS).
Impesud
AI MLOps Project – A production-grade MLOps pipeline for scalable, reproducible, and cloud-ready machine learning with Spark, scikit-learn, MLflow, and LLM-powered analytics.
DakshRathi
A sophisticated sentiment analysis project that classifies YouTube comments into positive, neutral, or negative sentiments using advanced machine learning techniques. This production-ready solution combines LightGBM for high-performance sentiment classification with modern MLOps practices.
In this project, explore Machine Learning Workflow Tools Dive into MLflow and DVC for experiment tracking, model version control, and efficient ML project management. Hands-on experiments to master organizing and scaling workflows
vigneshwarmr
💓 Heart Disease Prediction API using FastAPI & MLOps This project demonstrates an end-to-end Machine Learning pipeline wrapped in a production-ready FastAPI service to predict the likelihood of heart disease based on medical features. It follows MLOps principles for reproducibility, scalability, and deployment.
KaushalprajapatiKP
A modular MLOps pipeline for end-to-end machine learning: automated data validation, feature engineering, multi-model training, MLflow/DagsHub tracking, and FastAPI deployment. Containerized, extensible, and production-ready for real-world projects. Explore code, docs, and notebooks for reproducible ML workflows.
MLOPs Production Ready Machine Learning Project
sudhinsuresh
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SHINU4RATHOD
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SejanMahmud76
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abhishek199677
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Sanket12122003
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SejanMahmud76
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hossamfarhoud
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osamashabih6960
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abishekP101
MLOPs-Production-Ready-Machine-Learning-Project
Abhiramkumarsoni
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Jyotitiwari24
Interactive portfolio showcasing 9+ production-ready machine learning projects with complete MLOps deployment.
The Production-ready machine learning (ML) project involves several key steps, processes, and tools that collectively form the practice of MLOps (Machine Learning Operations). MLOps is a set of best practices for managing the lifecycle of machine learning models in production, from development to deployment and monitoring.
garimatiwari2004
The Student Performance Prediction project utilizes Machine Learning (ML) to analyze student data and predict their academic performance. By integrating MLOps (Machine Learning Operations), the model is efficiently developed, deployed, monitored, and continuously improved in a production-ready pipeline.
Eshaanm4964
A fully automated, production-ready churn prediction pipeline built using modern MLOps practices. This project demonstrates the complete lifecycle of a machine learning system — from raw data ingestion to model deployment and monitoring.
anikul24
A collection of Machine Learning, AI, and Generative AI projects, covering end-to-end workflows from data engineering to model deployment. Includes classical ML models, modern deep learning techniques, and GenAI/agentic AI use cases. Focus on real-world applications in finance, data pipelines, and production-ready MLOps practices.
This project implements a complete, end-to-end MLOps pipeline to automatically train, evaluate, and package a machine learning model for predicting California housing prices. The core focus is on automation and reproducibility, bridging the gap between model development in notebooks and a production-ready, deployable asset.
moeedfaiz
Customer Churn MLOps is an end-to-end machine learning pipeline for predicting customer churn using tabular data. It integrates DVC for data/model versioning, MLflow for experiment tracking, FastAPI for model serving, and GitHub Actions for CI/CD automation, making the project fully production-ready.
abhayryad
A production-ready Machine Learning pipeline designed to detect network intrusions and security threats. This project implements a full MLOps lifecycle—from raw data ingestion (MongoDB) to model deployment (Docker/AWS). It features a custom ETL framework, automated data validation, and experiment tracking via MLflow, achieving 95% accuracy on test.
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