Aerospace LLM Evaluation with Domain Expert Scoring
Combine automated checks with expert scoring, traceable tests, and release gates to evaluate aerospace LLMs and prevent unsafe deployments.
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Articles tagged “MLOps” on Ryshe.
Combine automated checks with expert scoring, traceable tests, and release gates to evaluate aerospace LLMs and prevent unsafe deployments.
Secure Azure OpenAI traffic with HTTPS on every hop: enforce TLS, configure private endpoints and DNS, and test allowed/blocked paths.
Measure MRR@10 for Azure AI Search: define relevance, build judged queries, compare retrieval setups, and track production metrics.
Compare BLEU with learned metrics (COMET, BLEURT, BERTScore) for translation QA; combine scores with deterministic checks and human review.
Don't treat connection metadata as proof; verify identity, network path, retrieval, rotation and audits before production.
Use source-aware COMET scoring as a QA gate to auto-approve, flag, and route translations with auditable thresholds.
Treat EASA alignment as an early task: define AI role and boundaries, map requirements, use runtime guards, and retain traceable evidence.
How AI scorecards spot supplier risk - focusing on data quality, explainable models, and governance for reliable action.
AI methods to detect and stop free-trial abuse: anomaly detection, identity clustering, behavior scoring, velocity checks, pattern matching.
Connect telemetry, maintenance, quality, and supplier data in Azure to score risk, trigger alerts, and power role-based reviews.
Use Azure to unify plant data, model takt and bottlenecks, run what-if scenarios, and route only approved line changes to operators.
Treat Azure AI workflow evaluation as continuous: batch tests, shadow runs, human review, threshold tuning, and cross-system validation.