There are 11 repositories under mlops-project topic.
Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
End to end machine leanring project: This repository serves as a simplified guide to help you grasp the fundamentals of MLOps.
The project is a concoction of research (audio signal processing, keyword spotting, ASR), development (audio data processing, deep neural network training, evaluation) and deployment (building model artifacts, web app development, docker, cloud PaaS) by integrating CI/CD pipelines with automated tests and releases.
An end-to-end MLOps pipeline(CI/CD/CT/CM) project for training, versioning, deploying, and monitoring machine learning models using FastAPI, Kubernetes, MLflow, DVC, Prometheus, and Grafana.
The Machine Learning Zoomcamp teaches foundational and advanced ML concepts using tools like NumPy, Pandas, Scikit-Learn, TensorFlow, XGBoost, Flask, Docker, AWS, Kubernetes, and KServe. It covers regression, classification, evaluation metrics, neural networks, deployment strategies, and end-to-end projects to bridge theory and practice.
Consignment-Price Prediction project aims to develop a machine learning model that can accurately predict the price of consignment items based on various features and variables
Automated pipeline for energy consumption forecasting across Europe using Azure cloud and Databricks.
Explore MLOps excellence! This repository curates mini-projects demonstrating ML deployment, NLP, and Deep Learning. Discover CI/CD/CT pipelines, best practices, and dive into practical MLOps insights. Elevate your skills in deploying and managing cutting-edge machine learning applications.
Predictive maintenance can help companies minimize downtime, reduce repair costs, and improve operational efficiency. Developing a web application for predictive maintenance can provide users with real-time insights into equipment performance, enabling proactive maintenance, and reducing unplanned downtime.
This repo shows how to implement a simple image generation app that uses Jax-Implementation of a conditional VAE, Jax, fastapi, docker, streamlit, heroku, ec2, and cloudflare :smiley:
CI/CD ( Continous Deploy) With Github Actions, Docker & Docker Compose
Implementation of classification of grammatically correct sentences and wrong sentences, and integration of MLOps tools.
Human Pose Classifier using Vision Transformers (ViT) – end-to-end pipeline for preprocessing, training, testing, and deploying models with FastAPI/Streamlit and AWS integration.
Udacity NanoDegree Course 3 Project "Deploying a Machine Learning Model on Heroku with FastAPI"
Aircraft components are susceptible to degradation, which affects directly their reliability and performance. This machine learning project will be directed to provide a framework for predicting the aircraft’s remaining useful life (RUL) based on the entire life cycle data in order to provide the necessary maintenance behavior.
End to End Machine Learning MLOps Project for Credit Card Fraud Detection using Ensemble Models, Data and Model Versioning through DVC, Github Actions, and Deployment
A complete pipeline for sentiment analysis using Hugging Face Transformers and AWS services. The model can be run on both Streamlit Share Server and AWS (using S3 for storage and EC2 for deployment). This repository covers data preprocessing, model training, evaluation, and accurate sentiment prediction on reviews.
Handbook for putting applications in the cloud referencing DS and ML paradigms.
This project aims to detect fraudulent transactions by leveraging machine learning-based anomaly detection techniques, and to develop an automated system that can monitor transactions in real-time, identify anomalies, and flag potential fraudulent transactions for further investigation.
A full-stack machine learning project that predicts house prices in Bangalore based on user inputs like location, BHK, bathrooms, and total square feet.
This project deploys a diabetes prediction model on AWS using MLOps principles. It features a Flask-based UI for user interaction and utilizes CI/CD pipelines for automated deployment. By leveraging AWS infrastructure, the project ensures scalability, version control, and monitoring of the deployed model.
A Machine Learning System that work on prediciton of flight Delay to take action Based on it
Using MLflow to deploy your RAG pipeline, using LLamaIndex, Langchain and Ollama/HuggingfaceLLMs/Groq
Complete end-to-end MLOps implementation for training, maintaining and monitoring a machine learning model that predicts droughts.
An application for violent threat detection
An MLOps project for predicting illnesses based on weather conditions
Testing out ClearML.
A pipeline using Kedro to orchestrate the deployment of a deep learning transformer model for classifying toxic comments. This project integrates data preprocessing, model training, and deployment into a streamlined and reproducible workflow, enabling efficient handling of the toxic comment classification problem in NLP.
An end-to-end machine learning project predicting bank customer churn with a Gradient Boosting Classifier. It features a complete pipeline for data processing, model training, and real-time predictions via a Flask API. SMOTE is used for handling imbalanced data, and MLflow is integrated for model tracking.
MLOps System for Credit Ranking Project
MLOps Loan Approval Prediction System
Anomaly detection in transactions means identifying unusual or unexpected patterns within transactions or related activities. These patterns, known as anomalies or outliers, deviate significantly from the expected norm and could indicate irregular or fraudulent behaviour.