The platform's monitoring system is designed as a multi-layered solution for key pain points in AI application development:
- fault diagnosisLamatic.ai provides node-level execution logs that show exactly which process (e.g., data preprocessing, model inference) is abnormal, along with error codes and input samples, while traditional "black box" AI development makes it difficult to locate the source of errors.
- performance analysis: The monitoring panel displays the time consumption distribution of each node in real-time, helping to identify bottleneck points (e.g. high latency of external API calls). Combined with the historical data analysis function, the price/performance difference between different model versions (GPT-3.5 vs. GPT-4) can be evaluated.
- Effectiveness Tracking: For the output quality fluctuation problem unique to generative AI, the system records the complete input-output correspondence and supports automatic annotation based on keyword/sentiment analysis, which facilitates the discovery of cases where the model is illusory or deviates from the business requirements.
Development teams can set up customized alarm rules (e.g. triggered when error rate > 5%) and receive real-time notifications via Slack integration for proactive O&M.
This answer comes from the articleLamatic.ai: a hosted platform for rapidly building and deploying AI intelligencesThe
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