What is AI Observability?

Most LLMs tend to be a black box which doesn’t really lend well to large scale enterprise production applications. AI observability is the collection of statistics, performance data, and metrics from every part of the system. AI Observability is reliant not only on metrics, but also on how well issues can be explained when something eventually goes wrong. The result of an end-to-end observability pipeline is that the organization will get timely insights about changes to data and model behavior in production. This is especially useful for surfacing common ML issues such as data drift, stale models, and data quality changes. These signals can be fed back to the ML processes and accelerate the model development lifecycle.

The Avaamo Solution

Avaamo’s AI Observability layer creates feedback loops that inform various stakeholders of the health and quality of the data, model, and predictions. These feedback loops collect the data necessary to ensure that the model’s performance and customer experience do not degrade over time.

The key challenges with AI Observability that we solve

Model Monitoring: Models deteriorate over time and usage and its critical to understand the drift – where a model is failing and how to improve it. Monitor and validate all stages of the pipeline of the ML models.

Going beyond Accuracy: Accuracy as a metric only provides a part of the story. What’s needed is explainability of the model predictions in simple terms.

RLHF: Ability to include humans in the decision-making process via simple yet unbiased mechanisms.

Some of the Avaamo’s key AI Observability capabilities:

AI Drift: Monitor and validate all stages of the pipeline of the ML models. Provides detailed metrics on LLM agent performance, data drift, stale models, and data quality changes enabling businesses to evaluate and enhance their LLM experiences.

Explainable AI: Human readable explanations for all model predictions so business user and developers can understand the “why and how” behind the models

Fairness: explanations for ML model predictions and the ability to include humans in the decision-making process and mitigate bias

Analytics: Provides detailed metrics on LLM agent performance and data quality changes enabling businesses to evaluate and enhance their LLM experiences.