Backprop

Open Backprop, start a project, and turn a model into a live service in minutes. Pick a starter template or bring your own artifact from PyTorch, TensorFlow, scikit-learn, or ONNX. Use the visual builder to chain pre-processing, the model step, and post-processing on a canvas. Drop in tokenizers, image transforms, or custom Python nodes, then validate everything with the built-in request console. When it’s ready, publish a secure HTTPS endpoint, grab the key, and call it from your app with curl, Python, or JavaScript. Version every change, promote builds from staging to production, and roll back instantly if a release misbehaves.

If you need customization, launch a training run directly from the same workspace. Attach data from S3, GCS, or a local upload, define splits, and choose the compute profile that matches your budget. Configure hyperparameters, seed values, and early stopping without writing orchestration code. Track loss curves and validation metrics in real time, compare runs side by side, and register the best checkpoint with a single click. Promote the trained weights to an endpoint, run a shadow deployment to verify behavior on live traffic, then ramp users with A/B or canary rules. Schedule recurring retrains on fresh data and auto-archive stale models to keep spend under control.

Once services are live, operate them from a single control panel. Watch latency, throughput, and error rates, drill into request traces, and inspect payload samples to debug edge cases. Enable autoscaling based on concurrency or tail latency; keep warm pods ready for bursty workloads; switch between CPU and GPU profiles on a schedule to save costs outside peak hours. Set budgets and alerts that post to Slack or email. Manage secrets for API keys and database URLs, assign roles to teammates, and require approvals for production changes. Integrate with Git-based CI so every merge can build, test, and deploy a new model image, or manage infrastructure via Terraform for repeatable environments.

Here are a few practical flows to ship value fast. For content teams: spin up a summarization or rewriting service, add a profanity filter, and expose both as endpoints that your CMS calls during publish. For product squads: embed a recommendation model into your mobile app, throttle traffic during launch, and log feedback for later retraining. For operations: deploy a demand-forecast service that scores hourly via a scheduled job and writes results to your warehouse. For game studios: add a toxicity classifier to chat plus a reward model to power progression; monitor shifts and retrain weekly. In each case, you assemble the pipeline on the canvas, run smoke tests with sample inputs, publish the endpoint, and wire it into your stack with the provided SDKs—no custom serving code required.