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🐶 A tool to package, serve, and deploy any ML model on any platform. Archived to be resurrected one day🤞

🏷️ Git Tag Ops. Turn your Git repository into Artifact Registry or Model Registry.

A curated list of awesome open source tools and commercial products for ML Experiment Tracking and Management 🚀

MLOps for deploying a Credit Risk model

A simple app to help you find relevant machine learning papers to read.

An end to end ML project. Using MLflow for experiment tracking and model registry. Prefect for workflow orchestration. S3 for artifacts storage. AWS Lambda/ ECR for serverless model serving. AWS REST API gateway as endpoint to lambda function. GitHub Actions for CI/CD.

Hopsworks Machine Learning Api 🚀 Model management with a model registry and model serving

Part of the Hydrosphere.io project.

Newron is a data-centric ML platform to easily build, manage, deploy and continuously improve models through data driven development.

Experiment tracking and model registry in the images segmentation project

Production-grade MLOps: Model deployment, monitoring, feature stores, and ML pipelines for real-world AI systems.

An auto-updating AI Model Catalog available as a Golang Package, CLI Tool, or Server (RESTful, WebSockets, SSE).

Production MLOps pipeline for Paris bike traffic prediction. Airflow orchestration, MLflow tracking (Cloud SQL), FastAPI deployment. Features: automated ingestion, drift detection, champion/challenger models, Prometheus+Grafana monitoring, Discord alerts. 15 Docker services locally.

Centralized Feature Store built on DVC for ML feature versioning, validation, and sharing. Includes MLflow integration for experiment tracking and Kubeflow Pipeline components for production ML workflows.

Built an E2E MLFlow Pipeline & hosted on AWS.

Experiment tracking and model registry in the NLP project

This project is part of the CPE393 coursework on the topic of the MLOps final project.

An end-to-end MLOps pipeline for emotion detection. Features data versioning with DVC + AWS S3, model training and evaluation with MLflow, CI/CD via GitHub Actions, FastAPI serving, Docker containerization, AWS EC2 deployment, and experiment tracking on DagsHub.

Simple and secure model registry for storing, versioning, and serving ML models using S3 and MongoDB.