| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ~Get Your Files Here ! | |||
| 01. Evaluating foundation model outputs | |||
| 01. Evaluation dimensions beyond traditional ML.mp4 | 8.6 MB | ||
| 01. Evaluation dimensions beyond traditional ML.srt | 6.5 KB | ||
| 02. Bedrock evaluations framework and LLM-as-a-judge.mp4 | 7.7 MB | ||
| 02. Bedrock evaluations framework and LLM-as-a-judge.srt | 6.2 KB | ||
| 02. User feedback, quality assurance, and deployment validation | |||
| 01. User-centered evaluation and feedback collection.mp4 | 7.6 MB | ||
| 01. User-centered evaluation and feedback collection.srt | 6.7 KB | ||
| 02. Continuous evaluation workflows with Step Functions.mp4 | 4.8 MB | ||
| 02. Continuous evaluation workflows with Step Functions.srt | 4.5 KB | ||
| 03. Demo Automated quality gates.mp4 | 9.8 MB | ||
| 03. Demo Automated quality gates.srt | 4 KB | ||
| 03. Evaluating RAG systems and agents | |||
| 01. Evaluating retrieval quality in RAG systems.mp4 | 9.1 MB | ||
| 01. Evaluating retrieval quality in RAG systems.srt | 7.7 KB | ||
| 02. Demo Running a Bedrock Knowledge Base RAG evaluation job.mp4 | 19.8 MB | ||
| 02. Demo Running a Bedrock Knowledge Base RAG evaluation job.srt | 7.3 KB | ||
| 03. Agent performance evaluation frameworks.mp4 | 6.1 MB | ||
| 03. Agent performance evaluation frameworks.srt | 5.3 KB | ||
| 04. Demo Testing and evaluating a Bedrock agent.mp4 | 22.4 MB | ||
| 04. Demo Testing and evaluating a Bedrock agent.srt | 5.8 KB | ||
| 04. Troubleshooting content handling and FM integrations | |||
| 01. Resolving context window overflow and content handling issues.mp4 | 7.7 MB | ||
| 01. Resolving context window overflow and content handling issues.srt | 6.7 KB | ||
| 02. Diagnosing FM API integration issues.mp4 | 6.5 MB | ||
| 02. Diagnosing FM API integration issues.srt | 5.7 KB | ||
| 03. CloudWatch GenAI observability and end-to-end tracing.mp4 | 5.1 MB | ||
| 03. CloudWatch GenAI observability and end-to-end tracing.srt | 4.4 KB | ||
| 04. Demo CloudWatch GenAI observability.mp4 | 11.5 MB | ||
| 04. Demo CloudWatch GenAI observability.srt | 3.6 KB | ||
| 05. Troubleshooting RAG systems and prompt maintenance | |||
| 01. Troubleshooting retrieval system issues.mp4 | 8.7 MB | ||
| 01. Troubleshooting retrieval system issues.srt | 7.4 KB | ||
| 02. Vector search optimization and ANN algorithms.mp4 | 8.6 MB | ||
| 02. Vector search optimization and ANN algorithms.srt | 6.7 KB | ||
| 03. Demo Diagnosing RAG retrieval problems.mp4 | 15 MB | ||
| 03. Demo Diagnosing RAG retrieval problems.srt | 5.8 KB | ||
| 04. Prompt maintenance and template testing.mp4 | 4.3 MB | ||
| 04. Prompt maintenance and template testing.srt | 3.5 KB | ||
| 05. Prompt observability with CloudWatch and X-Ray.mp4 | 4.9 MB | ||
| 05. Prompt observability with CloudWatch and X-Ray.srt | 4 KB | ||
| 06. Exam preparation | |||
| 01. Practice question 1.mp4 | 9.5 MB | ||
| 01. Practice question 1.srt | 4.5 KB | ||
| 02. Practice question 2.mp4 | 6 MB | ||
| 02. Practice question 2.srt | 3.2 KB | ||
| 03. Practice question 3.mp4 | 8.8 MB | ||
| 03. Practice question 3.srt | 4.1 KB | ||
| 05. Troubleshooting prompt engineering problems.mp4 | 5.6 MB | ||
| 05. Troubleshooting prompt engineering problems.srt | 4.9 KB | ||
| 06. Prompt testing frameworks and systematic refinement.mp4 | 6 MB | ||
| 06. Prompt testing frameworks and systematic refinement.srt | 5.2 KB | ||
| 07. Demo Prompt refinement in Bedrock.mp4 | 14.8 MB | ||
| 07. Demo Prompt refinement in Bedrock.srt | 5.6 KB | ||
| 04. Deployment validation.mp4 | 4.9 MB | ||
| 04. Deployment validation.srt | 4.8 KB | ||
| 05. Demo Deployment validation.mp4 | 8.3 MB | ||
| 05. Demo Deployment validation.srt | 4.1 KB | ||
| 06. Reporting and visualization with Amazon Quick Sight and CloudWatch.mp4 | 6.1 MB | ||
| 06. Reporting and visualization with Amazon Quick Sight and CloudWatch.srt | 5 KB | ||
| 03. Demo Bedrock model evaluations.mp4 | 17.5 MB | ||
| 03. Demo Bedrock model evaluations.srt | 7 KB | ||
| 04. Advanced evaluation.mp4 | 9.6 MB | ||
| 04. Advanced evaluation.srt | 8.1 KB | ||
| 05. Human feedback and LLM-as-a-judge.mp4 | 8.5 MB | ||
| 05. Human feedback and LLM-as-a-judge.srt | 6.9 KB |
AIP-C01: Testing, Validation, and Troubleshooting
https://WebToolTip.com
Released 7/2026
By Arpad Toth
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Advanced | Genre: eLearning | Language: English + subtitle | Duration: 2h 2m 11s | Size: 299.2 MB
Generative AI applications require evaluation, testing, and monitoring to ensure they perform reliably in production.
Generative AI applications require evaluation, testing, and monitoring to ensure they perform reliably in production. In this course, AIP-C01: Testing, Validation, and Troubleshooting, you'll gain the ability to evaluate, troubleshoot, and validate generative AI workloads on AWS. First, you'll explore how to evaluate foundation model outputs using Bedrock's LLM-as-a-judge evaluations, human feedback workflows, and RAG and agent evaluation frameworks. Next, you'll discover how to build automated quality gates with Step Functions, validate deployments using synthetic user workflows, and report on model performance with CloudWatch and Quick Sight. Finally, you'll learn how to troubleshoot common issues, including FM API integration errors, prompt engineering problems, and RAG retrieval quality using CloudWatch GenAI Observability and the Bedrock Agent trace. When you're finished with this course, you'll have the skills and knowledge of generative AI testing, validation, and troubleshooting needed to evaluate and maintain production-grade GenAI applications on AWS.
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