The purpose of this project's design, development, and structure is to create an end-to-end Machine Learning Operations (MLOps) lifecycle to classify an individual's level of obesity based on their physical characteristics and eating habits.

MLOps for deploying a Credit Risk model

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.

This is likely featuring projects and resources related to AI. It may include code examples, tutorials related to AI concepts, algorithms, and applications.

Predict bike-sharing demand using machine learning pipeline for MLOps-Zoomcamp project, optimizing bike distribution and availability.

MlOps Zoomcamp final project

Repository contains the detail about ML model deployment and building end-to-end ML pipeline for production

A machine learning program that estimates the amount of loan to be issued as seen in the dataset given. This program uses a simple hist gradient boosting regressor algorithm to estimate the likely loan user should be eligible for.

Machine learning classification model with streamlit deployment.

End to End Machine Learning Observability Project

DriftRadar-Vision is an advanced, production-ready drift monitoring and adaptive retraining pipeline for vision models.

Comparison between several Python data profile libraries.

Evidently AI in tracking, analyzing, and visualizing machine learning model performance and data drift ensure their reliability over time.

ML Monitoring with EvidentlyAI

A production-grade, end-to-end MLOps platform designed to automate the complete lifecycle of a financial fraud detection system. This repository demonstrates how to transition from a static ML model to a self-healing, automated production system.

la partie Monitoring & Observabilité (User Stories 7.1 & 7.2) du projet de plateforme de scoring/prediction ML. Cette structure combine la surveillance des performances API (Prometheus + Grafana) et la détection de dérive des modèles (EvidentlyAI).

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.

Automation of Iris flower classes Mlflow experimental logging and prediction

MLOps project to predict 30-day hospital readmissions for BPJS patients in Indonesia and includes an interactive dashboard for regional and demographic analysis.