There are 1 repository under evidentlyai topic.
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.
This is likely featuring projects and resources related to AI. It may include code examples, tutorials related to AI concepts, algorithms, and applications.
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.
🎇✨ 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! 🎆🔥
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 final 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.
Machine learning classification model with streamlit deployment.
mlops zoomcamp capstone project
This repository is used to serve and monitor the stock predictor model.
End-to-end ML project that show cases model training, model deployment to Azure, CICD & MLOps using Docker and Github Actions.
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
Project to deploy ML model using Docker and Kubernetes
Automation of Iris flower classes Mlflow experimental logging and prediction
Building a full cycle of Machine learning Prediction App for Air quality
Testing Evidently AI open source python library
ML Monitoring with EvidentlyAI
final project for Codigo Facilito MLOPS