Understanding AI Observability: Improve Efficiency, Security & Costs

In this video, Jason Mattis breaks down the fundamentals of AI observability, explaining its crucial role in managing and optimizing generative AI systems. Learn about the three core pillars—data monitoring, model explainability, and diagnostics—and how mastering these can help your organization ensure data privacy, maintain model accuracy, manage costs, and enhance overall AI performance.

You'll gain insights into:
Effectively monitoring sensitive data such as PHI, PII, and intellectual property.
Ensuring model transparency and preventing bias and drift.
Diagnosing performance issues like latency, cost management, and optimal model usage.
Jason also showcases how Kong adds significant value by seamlessly integrating AI observability into existing API management frameworks. With Kong Konnect, see practical examples of real-time analytics, dashboards, user insights, and detailed AI cost tracking, all designed to optimize developer productivity and consumer experience.

0:00 Introduction to AI Observability

0:18 Common Challenges in AI Observability

0:47 APIs and Generative AI Management

1:36 Data Monitoring in AI

2:07 Model Explainability Essentials

2:32 AI Diagnostics: Latency, Cost, and Usage

3:02 How Kong Enhances AI Observability

4:15 Kong Konnect Demo: Managing Language Models

Learn more about Kong: https://konghq.com

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