Multimodal AI Data Infrastructure

The complete multimodal AI data journey in one unified system: connect your data in-place, define transformations as computed columns, and query everything together.

Ingest

In-place

Store

Unified

Transform

Auto

Execute

AI Models

Vector

Version

Lineage

All orchestration, caching, and versioning automatic — define what you want, not how to build it

$pip install pixeltable

Try 10-Min Tutorial View Documentation

See how the 6-step data journey translates to real-world scenarios. Each example shows the complete flow from data ingestion to production deployment.

Examples

ESSENTIALS

USER STORIES

ADVANCED

Key Features

Multimodal Storage

Images, videos, audio, docs

Incremental Updates

Only process what changed

Vector Search

Built-in similarity search

Versioning

Time travel & lineage

Monday 9:00 AM: New autonomous vehicle data arrives from fleet

2TB processed, 50K frames prioritized in 30 minutes vs. 3 days manually

ml-engineer_example.py

1# Connect to existing data sources without migration
2vehicles = pxt.create_table('fleet.raw_data', {
3    'video': pxt.Video,           # S3 references, no data movement
4    'sensor_metadata': pxt.Json,  # From existing RDBMS
5    'route_id': pxt.String,
6    'weather': pxt.String
7})
8
9# Import weekend's data (2TB of video + metadata)
10vehicles.insert_from_s3('s3://fleet-data/2025-01-06/')
11vehicles.sync_metadata_from_db('postgresql://fleet_db/sensor_readings')
12
13# Automatic frame extraction with YOLOX detection
14frames = pxt.create_view('fleet.frames', vehicles,
15    iterator=FrameIterator.create(video=vehicles.video, fps=1))
16
17frames.add_computed_column(
18    detections=yolox(frames.frame, model_id='yolox_l', threshold=0.6)
19)
20
21# Quality assessment for annotation priority
22@pxt.udf
23def annotation_priority(detections: dict, weather: str) -> float:
24    edge_cases = ['fog', 'rain', 'construction']
25    weather_mult = 2.0 if weather in edge_cases else 1.0
26    confidence_penalty = 1.0 - detections.get('avg_confidence', 0.8)
27    return weather_mult * confidence_penalty
28
29frames.add_computed_column(
30    priority=annotation_priority(frames.detections, vehicles.weather)
31)
32
33# Send high-priority frames to Label Studio
34high_priority = frames.where(frames.priority > 1.5)
35pxt.io.sync_label_studio_project(
36    ls_project_name='highway-fog-annotations',
37    view=high_priority,
38    config=label_studio_config
39)