No Docker. No AWS. No DevOps headaches.
For data scientists, ML engineers, and any Python developer who just wants to ship.
Free forever plan · No credit card · Ships in weeks
You're a data scientist, not a DevOps engineer. Containerization, environment configs, port mappings — all to just share your model.
IAM roles, EC2 instances, load balancers — you just want a live URL. Instead you lose 3 days in AWS documentation.
Most ML builders give up and share a repo nobody can run. Your brilliant model never reaches the people who need it.
You deploy to HuggingFace Spaces and your connection link just doesn't appear. No clear URL, no clear endpoint. Pure guesswork.
You built a brilliant ML app in Streamlit or Gradio. Now what? Most data scientists spend 3 days fighting Docker, AWS, and servers just to share it. With MLShip, follow 3 simple steps and get a live URL in 60 seconds. No DevOps knowledge required. Ever.
Connect your GitHub repo and deploy instantly. Every git push triggers an automatic redeploy — your app is always live and always up to date. Branch deployments let your team test on dev before shipping to production. Every commit is linked to your model version and experiment results.
You deploy your ML app and users flood in. Then the questions start — "Why did the model give this result?", "How do I upload my data?", "What does this score mean?" MLShip's AI widget answers ML-specific questions 24/7, automatically, trained on your app's own context.
You run 50 model experiments over 2 weeks. Which performed best? MLShip auto-logs every training run and links each GitHub commit to model metrics and deployment version automatically. Compare results side by side and deploy your best model in one click. Nobody else connects GitHub commits to experiment results — this is unique to MLShip.
Upload your Streamlit or Gradio project. MLShip auto-reads requirements.txt, pyproject.toml, and environment.yml — installs everything and gives you a live URL with a clear connection endpoint in 60 seconds. Community templates let you deploy a starter app instantly. CPU/GPU selection built in. Clear error messages in plain English when anything fails.
Connect your GitHub repo and deploy instantly. Push to main → production redeploys automatically. Push to dev → private testing environment. Every commit is linked to your model version and deployment history.
Add a smart support layer to your deployed app in 5 lines of Python. The AI answers user questions 24/7 — why did the model predict this, how do I upload data, what does this output mean — and escalates only when it can't handle it.
Auto-log every model run. Every GitHub commit links to model metrics and deployment version automatically. Compare results side by side. Deploy your best model in one click. Simpler than MLflow, cheaper than W&B — built for indie builders.
Full REST API to manage all your deployments programmatically. Beautiful unified dashboard showing every app, every version, every metric in one place. Team collaboration features, usage analytics per app, and role-based access control.
Uptime alerts the moment your app goes down. Error rate tracking, response time monitoring, traffic analytics, and model performance metrics over time. Know before your users do when something breaks.
A public gallery of every ML app deployed on MLShip. Your model reaches the world. Other builders discover your work. Students showcase their projects. Researchers share demos. MLShip becomes the place ML apps live — and the place people go to find them.
Join data scientists, ML engineers, and Python developers who are done wasting days on deployment and want to get back to building.