n8n is a strong candidate for orchestrating ML workflows when you need a highly-customizable, low-code
automator. Use it to schedule ingestion, run preprocessing, call inference endpoints, and persist
predictions.
Typical components: – Ingest: pull data from APIs, databases, or message queues. – Preprocess: transform,
normalize, and validate — use code or dedicated nodes. – Infer: call a deployed model (serverless endpoint
or hosted inference API). – Store & Act: write predictions back to a DB, trigger alerts, or kick off downstream
automations.
For production ML, add observability: log input distributions, prediction latencies, and error rates.
Implement canaries on model rollout by routing a small percent of traffic to the new model for verification
before full rollout.
Deploy a small inference endpoint and create an n8n workflow that calls it on a schedule, logging results to a database..
