Deploy a Machine Learning Model as an API — in One Click
Training a model is half the job; shipping it is where most projects stall. OctOpus closes that gap: every winning model can be deployed to a hosted prediction API in one click, and bundled as a portable deploy.zip you can run on your own infrastructure. No Docker wrangling, no MLOps engineer, no notebook-to-production rewrite.
From validated winner to live endpoint
After OctOpus validates a model on a holdout, deployment is one click. The agent wires up the preprocessing, the model, and a prediction route, then exposes a hosted endpoint you can call with new rows. The same model also downloads as a model.pkl, so nothing is locked in.
Portable deploy.zip — run it anywhere
Alongside the hosted API, OctOpus bundles a deploy.zip containing the model and the code to serve it. Drop it into your own cloud, a container, or an on-prem box. For regulated teams that can't send data to third-party APIs, this keeps predictions entirely inside your environment.
Predict on new data without writing serving code
Upload a new CSV and OctOpus runs batch predictions through the trained pipeline, handling the preprocessing automatically. Whether you want a live API for real-time scoring or a one-off batch prediction, you never write serving code by hand.
Key capabilities
- One-click deploy of the validated winner to a hosted prediction API.
- Portable deploy.zip — ship the model to your own cloud or on-prem.
- Download the raw model.pkl — no lock-in.
- Batch predictions on new CSVs with automatic preprocessing.
- No Docker, no MLOps engineer, no notebook-to-production rewrite.
Frequently asked questions
How do I deploy a model built with OctOpus?
One click. Once OctOpus has validated a winning model on a holdout, you deploy it to a hosted prediction API from the run view. OctOpus wires up preprocessing, the model, and the prediction route for you.
Can I run the model on my own infrastructure?
Yes. Every deployable model bundles as a portable deploy.zip with the model and serving code, so you can host it in your own cloud, a container, or on-prem. You can also download the raw model.pkl. Nothing is locked in.
Do I need MLOps or DevOps skills?
No. There's no Docker setup, no Kubernetes, and no notebook-to-production rewrite. OctOpus handles the serving layer so a non-engineer can take a model from trained to live API.
Can I predict on new data without a live API?
Yes. Upload a new CSV and OctOpus runs batch predictions through the trained pipeline, applying the same preprocessing automatically. Use the hosted API for real-time scoring or batch mode for one-off runs.