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INIT-01ActiveLaunch: 2025-09Owner: Systems Team

Neural Pipeline

End-to-end training and inference pipelines for custom models. Versioned runs, metrics, and deploy in one place.

/ Overview

Neural Pipeline gives you a single surface for training, evaluating, and deploying custom models. Every run is versioned, every metric is queryable, and promotion to production is one click. We handle orchestration, checkpointing, and scaling so you can focus on the model.

/ The_Problem

Teams waste weeks stitching together training scripts, experiment trackers, and deployment pipelines. Model versions get lost. Reproducibility is a myth. We built Neural Pipeline so you get one system: from data to production, with full lineage and one-click deploy.

/ Version_everything

Every run is versioned: code, data, config, and artifacts. Reproduce any experiment or roll back a deploy to a previous run in one click. Full lineage from raw data to the model currently in production.

/ Built_for_scale

We run on your cloud or ours. Scale training jobs to hundreds of nodes, run evals in parallel, and serve with autoscaling and multi-region replication. No ops burden—we handle the infrastructure.

/ How_It_Works

Step 1

Define and run

Point at your repo or upload a config. We spin up jobs, capture logs and metrics, and version every run. Checkpoints are stored automatically so you can resume or branch from any run.

Step 2

Compare and evaluate

Side-by-side comparison of runs, custom metrics, and A/B evaluation. Pin baselines and track regressions. All evals are reproducible and tied to the same code and data versions.

Step 3

Deploy with one click

Promote a run to staging or production. We handle autoscaling, health checks, and rollbacks. Your model is served behind a stable API with monitoring and alerts out of the box.

/ Use_Cases

Research to production

Researchers iterate fast; engineering gets a clean path to deploy. Same pipeline, same metrics, no handoff docs.

Continuous retraining

Trigger training from new data or schedules. Compare new runs to the live model and promote only when metrics beat the baseline.

Multi-team experiments

One registry, shared baselines, and clear ownership. No more duplicate pipelines or lost experiments.

We cut our time from prototype to production from six weeks to four days. Neural Pipeline is the only place we run training and deploy.
Engineering Lead · Series B ML company
12K+
Runs / month
99.5%
Uptime
< 2min
Cold start

/ Roadmap

P1

Core pipeline

Training jobs, metrics, checkpointing

P2

Evaluation & comparison

A/B runs, custom metrics, dashboards

P3

Deploy & serve

One-click deploy, autoscaling, monitoring

P4

Multi-region

Replication and geo-routing

/ Gallery