Learn about challenges of running AI applications and how to address them with new generation of tools.
AI applications, whether simple model serving or inference jobs, differ from typical web and desktop applications; they are built around or include machine learning models. It sounds trivial, but has a substantial effect on applications, their deployment, monitoring and debugging.
Observability is a very familiar concept for DevOps and SRE teams. It is usually defined by its pillars: logs, metrics and traces. It would be fair to add profiles to the pillars, since they have proved to be instrumental and are now offered by many APM tools. I was fortunate to build one of the first production profilers, called StackImpact, and experienced first hand how evolving technology stacks require new tools to ensure performance and reliability.
Following the same logic, to cover AI applications and address the challenges presented above, the observability scope should be extended.
There are other aspects not covered here that may require special approach to monitoring. One example would be inference on edge devices.
Model performance, e.g. accuracy, is another aspect to consider in context of application observability. It is useful in the context of application monitoring, but only if model accuracy is available soon enough to detect and troubleshoot real-time issues. In practice, this is rarely the case.
Therefore, I consider model monitoring a part of model development and/or business analytics, rather than AI application and infrastructure monitoring. Additionally, model monitoring implementations are use case specific by nature.
The same logic can be applied to data drift and concept drift monitoring. If the drift is sudden and indicates a technical problem, detecting it will contribute to reducing MTTR and thus to reliability. Otherwise, e.g. for seasonal drift, it should be considered a part of model development, in the scope of adjusting the model to real world. Interestingly, as authors of this interview study note: "Surprisingly, participants didn’t seem too worried about slower, expected natural data drift over time — they noted that frequent model retrains solved this problem".
While traditional tools, like Prometheus and Grafana, are being used to monitor AI applications, as discussed above, they do not cover ML-specific aspects and therefore provide limited visibility for troubleshooting and optimizing model inference.
As far as the model performance is concerned, a number of startups are working on model monitoring solutions to help ML practitioners with improving and troubleshooting models on real world data.
In turn, we at Graphsignal are building tools to address observability challenges of AI applications and help ML engineers and SREs manage their AI stacks with confidence.