Multimodal Intelligence

A_ML_25: Multimodal Pricing Engine

Status: Checking
Bundleunknown
EnvironmentPRODUCTION
p95 Latency0 ms
Project Story

Price Prediction from Product Text + Image, Built for Trust

Use a single product sample to run the full serving path, then inspect reliability signals, fallback behavior, and deployment lineage.

Interaction Mode

Simple by default, deep when needed

Viewer mode keeps the demo focused. Engineer mode reveals schema, diagnostics, payload inspection, and controls for deeper validation.

Live Prediction

Result-First Experience

Ready for execution. Run a prediction to see output, feature behavior, and reliability insights.

Schemaaligned on demand
Feature Width--
Base Models--
Fallback Statewaiting
Predicted Price
$--
Reference--
Delta--
Variant--
Canary MAE--
Confidence Band
--

Run a prediction to estimate uncertainty and reliability.

Text Signal --

Waiting for prediction.

Image Signal --

Waiting for prediction.

Numeric Signal --

Waiting for prediction.

Live Target Schemasample_id, catalog_content, image_link
Feature Shape(1, --)
Total API Invocations0
Error Rate0.00%
Feature Fallback Rate0.00%
Batch Quality Checks Passed0.00%
Image Fallback
Text Fallback
Error Rate
DQ Pass Rate
Raw Diagnostics JSON

          
Live Service Snapshot

          
Reliability and Monitoring

Live service quality snapshot

System StateChecking
Fallback StatusAwaiting run
Error Rate0.00%
Data Quality Pass Rate0.00%
How It Works

End-to-end MLOps Lineage

Prediction calls are traceable from feature extraction and ensemble scoring to immutable bundle lineage and deployment metadata.

GitHub push
GitHub Actions
DVC pull data
Train model
Immutable bundle
Docker build
HF Spaces deploy