Artículo: AMZ-B0FX2H43VY

Building Robust AI Evals: Proven Strategies for Testing, Monitoring, and Improving LLM Performance (Engineered: Data, AI, and DevOps)

Disponibilidad
En stock

Peso con empaque
0.48 kg

Devolución
Sí

Condición
Nuevo

Producto de
Amazon

Sobre este producto

Design effective evaluation frameworks that align with business objectives and technical requirements.
Implement core and advanced metrics for LLMs, including semantic similarity, multi-step reasoning, and multi-modal assessment.
Build modular, automated evaluation pipelines with logging, monitoring, and regression testing for scalable deployments.
Detect data drift, concept drift, and performance anomalies in production, and trigger timely retraining and re-evaluation.
Integrate safety, fairness, and compliance checks into all stages of evaluation, ensuring ethical and reliable model behavior.
Leverage human-in-the-loop and multi-evaluator strategies to capture nuanced model performance beyond automated metrics.
Scale evaluation practices across teams and projects while maintaining governance, traceability, and knowledge transfer.

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