AI Observability: What it is & Why it Matters
AI systems are being integrated rapidly into many different operations and public-facing services.
This is why it is more important than ever to ensure that these systems are running as they are supposed to, with a focus on explainability and responsibility.
This is where AI observability comes into play - a practice that goes deep into each part of AI systems and models to discover how they behave and why they generate certain outputs.
Keep reading to learn more about what AI observability is and why it matters for your organization’s responsible AI framework.
What is AI Observability?
AI observability is the practice of understanding how AI models, systems, and other tools behave. It goes beyond traditional monitoring by diving much deeper into why AI systems produce the outputs they do and whether or not the output is accurate, high-quality, and safe.
This practice focuses on the collection, measuring, and analysis of various types of data, which include logs, traces, and metrics. These elements contribute to a comprehensive understanding of an AI system’s internal state, enabling professionals to gain insight into behavior and performance issues should they arise.
Observability is more than just tracking performance and monitoring outputs – it is understanding why it operates the way it does and ensuring that it is in a reliable and ethical manner.
The key components of AI observability include:
Logs: These are timestamped records of events within systems that come with surrounding context for use in troubleshooting and debugging.
Traces: This is the end-to-end journey of a user request as it moves through the system from whichever interface or application they are using through the entire AI architecture and back to the user.
Metrics: These are fundamental measures of system health over time, including latency and memory usage.
Token Usage: This is the number of tokens, or language units understood by AI models, that are processed by an LLM. Tokens directly impact the cost and response latency of LLM applications.
Model Drift: Changes in response patterns and other data can impact AI behavior over time, causing it to degrade performance.
Why Does AI Observability Matter?
AI systems are complex and probabilistic, which means they require robust evaluation processes to ensure reliable and accurate outputs. Often, AI is deployed as a black box with organizations focusing on the input and the output, but not what happens in between. This can lead to potential issues internally, and deterioration that can go unnoticed.
Data drift and bias are among the issues that could arise should AI observability practices be lacking, and they are known to invade systems without professionals knowing. Outputs could seem normal, but the internal decision-making process may be degrading due to changes in prompts, model updates, or shifts in user query patterns.
Observability also helps control costs of AI systems and applications. This practice tracks token usage and memory consumption in order to enable organizations to gain more insight into how the AI is interacting with the technology surrounding it. Sometimes, IT teams might find that AI is not using all of the energy available to it, or that it is using too much. Resource allocation is an important way for organizations to ensure top performance and lower costs.
Without observability practices, or understanding why AI produced an output or decision, organizations can suffer major reputational and financial consequences.
Essentially, AI observability helps to answer the questions:
Why did the system produce this response?
Is the system producing accurate and unbiased information?
How much is AI costing the organization?
These practices contribute to a robust responsible use framework and enable further compliance for organizations that have integrated AI. For more guidance and support on your AI journey, reach out to our team to explore innovative solutions and in-depth training.
“AI is a tool. The choice about how it gets deployed is ours.”
– Oren Etzioni, Computer Scientist
Comments