An ML experiment tracker written in Julia.
Deploy and manage ML/AI infrastructure on your own servers. Slothfully simple.
Automate the tracking, downloading, and analysis of AI papers from HuggingFace. Stay updated with cutting-edge research effortlessly! 🐙📄
This Laravel package automatically logs the currently logged-in user's ID to the created_by, updated_by, deleted_by, and restored_by fields of your Eloquent models. It also automatically timestamps the restored_at field when a model is restored. This simplifies the tracking of data modifications and provides valuable auditing capabilities.
How to track model changes in Django and execute dependent workflows
Automated catalog that fetches OpenRouter cloud models and inspects their capabilities with clickable markdown tables for reference.
🏆 A living ecosystem for diffusion models - Automatically tracking, benchmarking, and organizing foundation diffusion models and their fine-tuned variants. Features real-time updates, unified evaluation, and comprehensive resources.
Covers essential features like model tracking, versioning, and experiment, providing a foundation for efficient ML project lifecycle management.
Framework for managing machine learning models using MLFlow, focusing on advanced model tracking, versioning, and experiment management.
MLflow adapter for CrateDB.
End-to-end MLOps pipeline tracking wine quality prediction models with MLflow & FastAPI.
LifeSavAR transforms your smartphone and HoloLens into a lifesaving tool, offering immediate, augmented reality-guided CPR and AED instructions in emergencies. It's your pocket-sized first responder, empowering you to act confidently and effectively when every second counts.
Academic MLflow workshop extended with reproducible experiment tracking for Diabetes regression and MNIST classification.
Live dashboard tracking the latest AI models across providers
A local-first autonomous LLMOps platform for continuous LLM evaluation, failure diagnosis, and self-healing using local models.
An end-to-end MLOps pipeline for automating the training, deployment, and monitoring of a machine learning application designed to assess mental well-being signals based on behavioral data.
This project demonstrates how to track, manage, and visualize machine learning experiments using MLflow and integrate with DagsHub for version control and collaboration. It covers experiment tracking, model logging, and reproducible experiments in a collaborative environment.
This project automates the end-to-end machine learning training pipeline using Jenkins, ensuring seamless integration and automation. When new data arrives or code is updated, the pipeline automatically preprocesses the data, trains a machine learning model, evaluates its performance, saves the model with versioning, and logs the entire workflow.
Daily snapshots, CSV releases, dashboard, and API for Hugging Face models, arXiv papers, and LMArena rankings.
Credit Scoring model using XGBoost, tracked with MLflow, and explained using SHAP for interpretability.
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