Why Pixeltable
AI Data Infrastructure, Simplified
Transform complex multimodal data pipelines into intuitive table operations. Unified storage for text, images, video, and audio with automatic versioning and indexing.
Model Development
Define models as table operations. Supports custom Python UDFs. Integrates with popular ML frameworks. Automatic data lineage tracking.
Experiment Tracking
Version control for data and models. Compare experiments with table operations. Reproduce results through automatic lineage and provenance.
Incremental Pipelines
Efficient updates to data and models. Only recompute changed dependencies. Supports complex DAGs. Automatic caching and lazy evaluation.
Perfect For
LLM/RAG Developers
Focus on prompt engineering, not plumbing. Build incremental production-ready RAG systems with automatic document processing, embedding management.
Computer Vision Engineers
Build and deploy CV pipelines in days, not months. Manage video processing, frame extraction, annotations, and model inferences through a simple table interface.
MLOps Engineers
Deploy to production without rewrites. Automatic versioning, lineage tracking, and incremental updates work the same from laptop to cloud. Zero infrastructure management overhead.
Data/Research Scientists
Experiment faster, iterate smarter. Let Pixeltable handle data processing across all modalities while you focus on modeling. Compare experiments and reproduce results with confidence.
Documentation
API Reference
Blog
Pixeltable Blog
Contact
Open Source
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Saturday, February 25 2023
📈 Pydeck: High-scale spatial rendering in Python
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Pierre
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