Artificial intelligence (AI) models face complex challenges when deployed in production. Model drift, hallucinations in large language models, data quality issues, infrastructure problems, and even malicious prompt injections can undermine the effectiveness and reliability of AI models. While traditional monitoring solutions fall short in addressing these issues, InsightFinder AI Observability offers a better solution.
InsightFinder AI Observability is built on top of the company’s patented Unsupervised Machine Learning (UML) technology. UML can help AI models perform more reliably in production environments, using proactive observability and real-time anomaly detection to tackle some of the most pervasive AI model failures.
The Biggest Challenges for Enterprise-Scale AI Models
- Model Drift: AI models can become outdated as the data they were trained on diverges from real-world data over time. This “model drift” can lead to inaccurate predictions, which is especially risky for applications in finance, healthcare, and security. Frequent model updates can exacerbate this issue, creating a need for continuous monitoring.
- LLM Hallucinations: Large language models (LLMs) are known for generating responses that are sometimes irrelevant or incorrect, commonly referred to as “hallucinations.” These errors undermine user trust and satisfaction, particularly in customer-facing applications.
- Data Quality: Data inconsistencies, missing values, and erroneous inputs can disrupt AI performance. Poor data quality often goes unnoticed until it significantly impacts a model’s accuracy.
- Infrastructure Issues: Enterprise-scale AI models depend on high-performance infrastructure. Servers, databases, GPUs, and data streaming must work seamlessly to support the model. Performance bottlenecks, high error rates, or hardware failures in infrastructure can all degrade the model’s availability and reliability.
- Security Concerns: Public-facing models are vulnerable to prompt injections and other types of attacks that can alter their behavior. These security concerns require rapid detection and mitigation to ensure safe AI model usage.
AI Observability Applies Unsupervised Machine Learning (UML) To Address AI Model Issues
Traditional monitoring systems use a technique called DBScan clustering analysis to detect anomalies. Although widely used, DBScan clustering has an unacceptably high rate of false positives. Fortunately, there is a better solution. InsightFinder AI uses unsupervised machine learning, particularly in the form of Unsupervised Behavior Learning (UBL), to identify anomalies and get to root cause analysis. Here’s how UBL enhances AI Observability:
- Real-Time Detection of Model Drift: AI Observability’s use of unsupervised machine learning for AI model observability allows continuous, real-time monitoring of AI model behavior by examining all data (model input/output, performance traces, infrastructure metrics) associated with the model without pre-labeled categories. Subtle shifts in data distribution, indicating model drift, can be detected immediately, enabling timely retraining and calibration before performance declines.
- Proactive Data Quality Surveillance: Unlike rule-based systems that rely on predefined triggers, UML approaches dynamically detect both known and unknown data quality issues. This enables the detection of unexpected issues without the need for pre-established rules, which is particularly useful in large-scale applications.
- Effective Anomaly Detection: AI Observability’s UML approach allows it to achieve a true positive rate of over 95%, making this method ideal for managing high-stakes AI environments.
- Root Cause Analysis with Causal Inference: UML goes beyond merely identifying anomalies—it can provide insights into root causes. For instance, AI Observability correlates anomalies with infrastructure metrics, such as GPU usage and server load, which allows organizations to quickly identify and resolve issues, improving both Mean Time to Detect (MTTD) and Mean Time to Resolve (MTTR).
- Adaptive Security with UML: By continuously learning from data, UML is effective at detecting security threats like malicious prompt injections. AI Observability’s UML-based observability framework enables real-time threat detection, providing robust security for AI systems without requiring constant human intervention.
Final Thoughts
Unsupervised machine learning provides a foundation for proactive observability, addressing real-world production challenges with AI models. With tools like AI Observability, enterprises can maintain robust, reliable AI performance and adapt quickly to evolving AI landscapes.
Want to see more? Register here to join the AI Observability Beta Program.
