Markdown source

Dense Slides: LLM Observability, Evaluation, Experimentation Platform — Dat Ngo, Arize

Source Video

LLM Observability, Evaluation, Experimentation Platform — Dat Ngo, Arize

Method

This deck is slide-only. The existing captured video frame set supplies candidate frames, then local OpenCV rejects sponsor/title/speaker-only frames, crops visible slide surfaces, deduplicates, and saves the cropped slide images.

Cropped Visible Slides

• Recreated text/layout view: open HTML recreation
• AI slide classifier: content_slide confidence 0.98
• Text source: advanced OCR rapidocr-live/bright-screen/contrast reconciled by agent.
• OCR decision: ready — Diagram slide with multiple boxed text regions and smaller body copy that is better suited for OCR than manual transcription.

Slide text:

Observability

What is happening in my application? Can I root cause down into the problem?

Evaluation

How well is the AI product that I've built, actually performing according my criteria?

Experimentation & Improvement

The ultimate goal of observability and evaluation is to know where to iterate

and know where to improve the system

Classification audit: raw/sources/slide-ai-classification/dense/JsCCrBF7F1g/audit.json