Задачи по оценке моделей в Amazon Bedrock | AWS Certified AI Practitioner (AIF-C01)

AWS Certified AI Practitioner (AIF-C01) candidates: this video explains how to set up practical model evaluation jobs in Amazon Bedrock and what to choose for exam-style scenarios and real projects. AWS Certified AI Practitioner (AIF-C01) learners will gain pragmatic decision rules and hands-on criteria to pick automatic evaluation or a human workforce for foundation model testing. What you’ll learn: How Amazon Bedrock automatic evaluation jobs work in practice (datasets, metrics, scale considerations) When to select a human workforce evaluation for subjective or nuanced tasks Recommended dataset selection and labelling best-practices for evaluation jobs Common metrics (accuracy, F1, BLEU, human agreement) and how to interpret them Selection criteria: cost, latency, task type, quality expectations, and sample size How to combine automatic and human evaluation for reliable validation Exam decision rule: Prefer automatic evaluation for large-scale, objective metric needs; prefer human workforce when tasks require subjective judgement, contextual understanding, or nuanced error analysis. Suggested next lesson: SageMaker Ground Truth vs. Ground Truth Plus — practical differences for labelling projects. Subscribe for weekly AIF-C01 study and practicum content to bridge exams and real-world skills. #AmazonBedrock #AIFC01 #ModelEvaluation This content is for educational purposes only. It is independent content and is not affiliated with, endorsed by, or sponsored by Amazon Web Services. AWS certification exam guides, domains, and formats can change; refer to official AWS certification resources for current information.