Problem
We are wiring a Java client to push updated intent classification models into NICE Cognigy.AI and the pipeline keeps choking on the atomic PUT request. We need to sync deployment events with our external MLOps pipelines via callback handlers, but the validation logic keeps failing before the cache warming triggers even fire.
Code
Map<String, Object> payload = new HashMap<>();
payload.put(“modelVersion”, “intent_v4.2.1”);
payload.put(“thresholdMatrix”, Map.of(“precision”, 0.88, “recall”, 0.91, “overlapScore”, 0.04));
payload.put(“fallbackRouting”, “agent_transfer_high_conf”);
payload.put(“mlopsCallback”, “https://internal-pipeline.corp/deploy-sync”);
payload.put(“cacheWarm”, true);
HttpPut put = new HttpPut(“https://api.cognigy.ai/v1/models/deploy”);
put.setEntity(new StringEntity(new Gson().toJson(payload)));
put.setHeader(“Content-Type”, “application/json”);
CloseableHttpResponse resp = client.execute(put);
Error
The endpoint throws a 422 Unprocessable Entity with validation_failed: threshold_matrix_bounds_exceeded and it’s dropping the audit log entry entirely. Precision recall checking isn’t running before the overlap score verification pipeline, so inference latency failures keep happening during bot scaling.
Question
How do we structure the threshold tuning matrix so it passes format verification without tripping the maximum concurrent model limits? We’re missing the deployment validation logic that should run the precision recall checks before triggering the MLOps callback handler. The automatic cache warming triggers also skip when fallback routing directives are attached. Does the API expect nested objects for the matrix or flat key value pairs? It’s weird how the docs skip the exact JSON shape for the verification pipeline anyway.