Your model.
Live in 60s.
No DevOps.
You spent weeks training that model.
You shouldn't spend days deploying it.
One command turns your
.pkl
into a production HTTPS API — no Docker, no Kubernetes, no ops team.
Deployment shouldn't be
a second job.
ML engineers lose 40% of their time to infra that has nothing to do with the model. That's not a people problem. It's a tooling problem.
You trained the model in a notebook. Then came the Dockerfile, the ECR setup, the ECS task definition, the IAM role debugging. Days of work before a single prediction hits production.
Works on your machine. Breaks in the container. The version mismatch between your conda env and the cloud runtime costs hours you didn't budget for — every single deploy.
Retrained the model? Great. Now redo the whole process. Push new image, update the service, wait for rollout. Iteration velocity drops to zero when shipping is this painful.
Three steps.
Notebook to cloud.
No YAML. No IAM roles. No Stack Overflow rabbit holes.
Train in Jupyter exactly as you always do. Save with joblib or pickle. No code changes, no new imports, no rewrites. Your existing workflow is already compatible.
joblib.dump(model, "model.pkl")Mlgatee detects your framework, resolves dependencies, builds a container, and ships it to the cloud — fully automatic, in under 60 seconds. Zero config files touched.
mlgatee deploy model.pklA real HTTPS URL, live and callable from anywhere. Share with your team, integrate in any app, monitor from your dashboard. Versioned automatically on every redeploy.
POST /predict → JSON responseProduction Grade,
out of the box.
Everything you'd spend a week setting up manually is included by default.
Sklearn, PyTorch, TensorFlow, XGBoost Mlgatee identifies your stack and resolves all dependencies automatically. No requirements.txt needed.
Every endpoint is served over TLS. No self-signed certs, no API gateway config. Callable from day one, from anywhere.
Your endpoint scales with demand and back to zero when idle. You never pay for compute you're not using.
Latency, error rates, request counts — all tracked from deploy. No Datadog setup, no CloudWatch. Just a clean dashboard.
Every deploy is versioned. If your new model regresses, roll back in one command — no downtime, no drama.
Share your endpoint with engineers, PMs, or clients. Authenticated access and API key management built right in.
Stop configuring.
Start shipping.
Join ML engineers who are done fighting infrastructure. Apply for early access and we will personally reach out when your spot is ready.