DagsHub
FreemiumA unified MLOps platform that hosts code, data, experiments, and models for AI teams on open-source standards.
DagsHub is an AI platform that manages the full machine-learning lifecycle from data collection and curation through experimentation to model deployment. It combines Git-based code hosting with DVC data versioning, MLflow experiment tracking, and Label Studio annotation in a single interface. Teams can collaborate on multimodal datasets, reproduce experiments, and deploy models while keeping data on DagsHub Storage or connecting their own cloud buckets.
What is it
DagsHub is a collaborative MLOps platform that hosts code, data, experiments, models, and pipelines in one place.
What it can do
It versions datasets with DVC, tracks experiments with MLflow, annotates data with Label Studio, visualizes pipelines, and manages model registries and deployments.
Who is it for
Data scientists, machine-learning engineers, MLOps teams, and AI organizations that want an open-source-friendly, unified workspace for managing multimodal AI projects.
Key Features
Dataset Management & Versioning — Git-like data versioning with DVC
Stores multi-terabyte datasets outside Git while keeping version pointers in the repository, enabling full reproducibility and lineage tracking.
Experiment Tracking — MLflow-compatible logging
Provides a hosted MLflow tracking URI for every repository so teams can log parameters, metrics, artifacts, and compare runs without setting up a separate server.
Data Annotation — Label Studio integration
Supports AI-assisted, human-in-the-loop labeling for images, video, audio, text, LLM data, and medical imaging inside the same workspace.
Model Registry & Deployment — Lifecycle management for models
Manages model versions, statuses, and deployments while maintaining lineage back to the data and experiments that produced each model.
Use Cases
Version multimodal datasets alongside code
Keep large image, video, audio, text, and tabular datasets versioned with code so every experiment is reproducible.
Track and compare ML experiments
Log parameters, metrics, and artifacts from training runs and compare them visually to find the best model.
Curate and annotate training data
Build high-quality labeled datasets for computer vision, NLP, and speech models without exporting data to separate tools.
Manage model lifecycle from staging to production
Publish model versions, link them to source data and experiments, and deploy to your own infrastructure.
Pricing plans
Frequently Asked Questions
Discussion
No comments yet. Be the first to start the thread.