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.

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

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.

Automated pipeline for energy consumption forecasting across Europe using Azure cloud and Databricks.

Implementation of classification of grammatically correct sentences and wrong sentences, and integration of MLOps tools.

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 😃

MLOps deploying house estimate model

Udacity NanoDegree Course 3 Project "Deploying a Machine Learning Model on Heroku with FastAPI"

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.

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.

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.

An application for violent threat detection

Official Course Webpage for CS175: Projects in AI

End-to-end machine learning project for diamond price prediction using Lasso Regression with 93% accuracy.

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.