Demos

These end-to-end demos demonstrate how to use the Iguazio AI platform, MLRun, and related tools, to address data science requirements for different industries and implementations.

Gen AI Demos

Demo	Description
Call center	This demo showcases how to use LLMs to turn audio files, from call center conversations between customers and agents, into valuable data — all in a single workflow orchestrated by MLRun. MLRun automates the entire workflow, auto-scales resources as needed, and automatically logs and ses values between the different workflow steps.
Banking LLM monitoring and feedback loop	This demo illustrates how to train, deploy, and monitor, and LLM using an approach described as "LLM as a judge".
Banking Agent Demo	This demo showcases a modular, production-grade banking customer service chatbot. It combines traditional machine learning (churn propensity) and large language models (LLMs) in a single, observable inference pipeline. The system features conditional routing based on guardrails (banking topic and toxicity filtering), and dynamically adapts model behavior using conversation history, sentiment, and churn risk.

ML Demos

Demo	Description
Fraud Prevention (Feature Store)	This demo shows the usage of MLRun and the feature store. Fraud prevention specifically is a challenge as it requires processing raw transaction and events in real-time and being able to quickly respond and block transactions before they occur. Consider, for example, a case where you would like to evaluate the average transaction amount. When training the model, it is common to take a DataFrame and just calculate the average. However, when dealing with real-time/online scenarios, this average has to be calculated incrementally.
Banking Agent Demo	This demo showcases a modular, production-grade banking customer service chatbot. It combines traditional machine learning (churn propensity) and large language models (LLMs) in a single, observable inference pipeline. The system features conditional routing based on guardrails (banking topic and toxicity filtering), and dynamically adapts model behavior using conversation history, sentiment, and churn risk.