Django ML Agent With n8n

I’m ready to move an experimental AI agent into production and want it running inside a clean Django code-base that can be triggered, monitored, and chained through n8n workflows. The core of the job is to wire up a small-footprint Machine Learning model (classification or regression—whichever you recommend for the data I’ll supply) so that it can accept REST or webhook calls from n8n, perform its inference, return a structured JSON response, and record the result in a PostgreSQL table.

Here’s the flow I have in mind: n8n fires a webhook, Django receives the request, hands it off to the ML component (PyTorch, scikit-learn, or TensorFlow—whichever best fits your proposal), then responds back to n8n while logging the transaction. I already run n8n on a separate VPS, so you’ll only need to expose the right endpoint and supply any required auth tokens. A lightweight admin panel for reviewing queued jobs and results will round things out.

What I need from you up front is a detailed project proposal that explains:
• the ML approach you plan to use
• how you’ll structure the Django app (apps, urls, views, serializers)
• the testing strategy (unit + integration)
• deployment steps (Docker or native—your call)

If the outline looks solid I’ll hand over a small anonymized dataset and the server credentials so you can get started right away.