MLOps for deploying a Credit Risk model
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.
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.
Repository contains the detail about ML model deployment and building end-to-end ML pipeline for production
Predict bike-sharing demand using machine learning pipeline for MLOps-Zoomcamp project, optimizing bike distribution and availability.
This is likely featuring projects and resources related to AI. It may include code examples, tutorials related to AI concepts, algorithms, and applications.
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.
End to End Machine Learning Observability Project
mlops zoomcamp capstone project
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.
MlOps Zoomcamp final project
Machine learning classification model with streamlit deployment.
Automation of Iris flower classes Mlflow experimental logging and prediction
End-to-end ML project that show cases model training, model deployment to Azure, CICD & MLOps using Docker and Github Actions.
Testing Evidently AI open source python library
🎇✨ MLOps Mini-Projects & Use Cases ✨🎇 Explore hands-on MLOps projects! 🚀 From ML and DL to Generative AI, see how I deploy and manage models using cutting-edge tools. 🔧💻 Regular updates as I experiment with new MLOps solutions! 🎆🔥
This repository contains code to simulate model monitoring. It uses Evidently to compute metrics, PostgreSQL to store the computed metrics and Grafana to display the metrics
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