Introduction
- Dennis Bov and Greg Apple presented a talk on observability for generative AI (Gen) systems.
- They discussed the challenges of moving Gen proofs-of-concept (POCs) to production and the importance of observability in addressing these challenges.
- The talk covered the key components of a Gen platform and the observability concerns related to each component.
Challenges of Moving Gen POCs to Production
- With traditional machine learning, the focus has been on accuracy, but with Gen, the overall quality of the system is more important.
- Factors like relevance, factual correctness, and robustness are critical, but difficult to achieve with the current state-of-the-art in Gen models.
- There have been several high-profile incidents of Gen models producing inaccurate or harmful outputs, highlighting the need for better observability.
- Additionally, performance and cost are major concerns, with Gen models often exhibiting much slower latencies and higher costs compared to traditional systems.
Key Components of a Gen Platform
- Foundation Model Hub: Provides access to various Gen models and includes features like access control, cost tracking, and failover.
- Retrieval Augmented Generation (RAG): Combines Gen models with information retrieval to provide context-aware responses.
- Orchestration: Handles higher-level application-level coordination, versioning, and governance.
- Underlying Infrastructure: The IT infrastructure that the Gen platform runs on.
Observability for Gen Platforms
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Foundation Model Hub:
- Observe API invocations, latency, token consumption, and other model-specific metrics.
- Implement cost tracking and throttling.
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Retrieval Augmented Generation:
- Observe the indexing and embedding processes.
- Analyze the quality of the retrieved context and its relevance to the prompts.
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Orchestration:
- Trace the execution of the Gen-based workflows.
- Observe the performance and cost of the orchestration components.
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Observability Tooling:
- Leverage CloudWatch for metrics, logs, and traces.
- Utilize features like alarm hierarchies, log pattern analysis, and embedding drift detection.
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End-User Feedback:
- Capture user sentiment (e.g., thumbs up/down) and associate it with the Gen system's outputs.
- Use CloudWatch Embedded Metric Format to correlate logs and metrics for this feedback.
Conclusion
- Observability is critical for the successful adoption and operation of Gen systems in production.
- A layered approach to observability, leveraging CloudWatch's capabilities, can help address the challenges of Gen platforms.
- Combining metrics, logs, and traces, along with advanced features like guardrails and end-user feedback, can provide a comprehensive observability solution for Gen systems.