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
End to End Machine Learning Observability Project
Machine learning classification model with streamlit deployment.
DriftRadar-Vision is an advanced, production-ready drift monitoring and adaptive retraining pipeline for vision models.
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.
ML Monitoring with EvidentlyAI
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.
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).
Project to deploy ML model using Docker and Kubernetes
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.
Bree Data Science Project : Detecting Fraud from Credit Users, using synthetic transactional data
Automation of Iris flower classes Mlflow experimental logging and prediction
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