How to Operationalize Your Own Customized Application for Monitoring LLMs with MLRun

LLM monitoring helps optimize for accuracy and efficiency, detect bias and ensure security and privacy. But common metrics like BLEU and ROUGE aren’t always accurate enough for LLM monitoring. By developing your own monitoring application, you can customize and tailor the metrics you need, monitor in real-time, integrate with other systems, and more. In this blog post, we explain how to do this with MLRun.

Monitoring generative AI applications and LLMs is an essential step in the AI pipeline. By monitoring, data professionals ensure models are accurate and bring business value. It also helps remove the risks associated with gen AI.

Overall, LLM monitoring can help:

• Manage resources and reduce operational costs.
• Optimize for efficiency and accuracy, ensuring model reliability at a given task and checking if it needs to go into another phase of development.
• Detect errors, biases, or inaccuracies in outputs, ensuring they meet quality standards.
• Identify and mitigate ethical issues like bias and toxicity, before they become public concerns.
• Ensure data privacy and security, to prevent data leakage, violation of privacy regulations, and more
• Meet compliance regulations.
• Understand how users interact with the model.
• Build trust among stakeholders.

There are many trackable LLM metrics, which can help meet the objectives detailed above. These include first-level metrics, model-related metrics, data metrics and more.

If the pipeline is: X -> Model -> Y

• Data metrics check X.
• Accuracy metrics check Y and sometimes Y | X (Y given X).
• Performance check the arrows.

Given this, the common metrics include:

• Performance Optimization – Latency, throughout, resource utilization (CPU/GPU memory usage), data drift, sensibleness and specificity.

• LLM Evaluation (Accuracy) – Perplexity, BLEU (Bilingual Evaluation Understudy), ROUGE (Recall-Oriented Understudy for Gisting Evaluation), METEOR (Metric for Evaluation of Translation with Explicit Ordering), F1 score and accuracy.
• Data Metrics – Data drift

Additional metrics that can be monitored include:

• User Engagement – Session length, token efficiency

• Ethical Compliance – Adherence to guidelines, like privacy, non-discrimination, transparency and fairness.

In addition to these, data engineers and scientists can also come up with their own metrics, based on use cases and requirements. This is valuable for monitoring LLMs, since these popular metrics don’t always cover unique LLM monitoring needs.

For example:

• Logic monitoring metrics, which evaluate the logical processes and decision-making pathways of a system. They include input classification, response consistency, error detection, decision pathway analysis, and performance measurements.
• Domain-specific metrics or evaluation methods, including industry-specific terminologies, contextual relevance, or specialized linguistic nuances.
• Bias detection algorithms that operate based on your organization’s ethical standards and regulatory requirements.

By developing your own monitoring application, you can monitor LLMs based on the metrics you need, to ensure your LLM is fully-optimized to your use case. This will ensure it brings business value and help avoid LLM risks that have technological and business implications.

By developing and deploying your own monitoring application you can:

• Tailor evaluation criteria to align closely with your specific use case or domain, maximizing business value.
• Incorporate real-time monitoring, alerting you about anomalies or performance issues as they occur.
• Integrate your monitoring application seamlessly with other internal systems or workflows
• Future-proof to adapt as new models and technologies emerge, keeping your application relevant and up-to-date.
• Generate customized reports tailored to your organization’s specific needs, providing actionable insights and data-driven decision-making.

Open-source MLRun provides a radically simplified solution, allowing anyone to develop and deploy their own monitoring application in a few simple lines of code. Inherit the `MonitoringApplication` class, implement one method and that’s it!

You can see the full tutorial with code snippets and examples in the MLRun documentation.

Get started with MLRun now.