Hi everyone,
I’m looking for guidance on how to deploy a neural-network model (binary prediction + probability) so that users can run live inference inside Workshop.
For reference this is my adapter:
Here’s what I have:
Model + adapter – The adapter expects a DataFrame with the same 60 fields as the Foundry object we want to score.
Basic join – I can publish the inference output, join it back to the object, and display the results in widgets. That works, but it’s static.
Interactive attempt – I published the model as a deployment and wrapped it in a Workshop Function. After manually mapping all 60 variables, the function returns nothing in Workshop, even though the deployment itself tests fine. I followed the Ontologize tutorial (https://youtu.be/TRIOCHJ6wdw?t=180), but I’m clearly missing a step.
Ideas I’m weighing:
Let the adapter read the Foundry object directly. Use the Object SDK inside the adapter and run predictions there.
Add a TypeScript function. Fetch the object in TS, call the Python inference endpoint, and return the enriched rows to Workshop.
Questions:
- What’s the simplest, recommended pattern for live inference in Workshop?
- Is there a faster way to bind ~60 inputs than adding them one by one?
- Between the two approaches above, which copes better if the object schema changes?
- If you’ve deployed something similar, could you share how you structured it?
Thanks a lot for any pointers!