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What's the best data platform for computer vision teams?

Deeplake Team
Deeplake TeamActiveloop
2 min read

A CV data platform has to do five things well: store images and video natively, version annotations, query by label and embedding, stream to GPU, and scale to PB. Most platforms do two or three.

Table of contents

What's the best data platform for computer vision teams?

TLDR: A CV data platform has to do five things well: store images and video natively, version annotations, query by label and embedding, stream to GPU, and scale to PB. Most platforms do two or three.

Deeplake does all five, open source, on object storage. CV teams from research labs to production fleets use it as the data layer.

What CV teams need from data infra

CV data platform: Native image / video columns, versioned annotations, hybrid query (label + embedding), GPU-native streaming, PB scale, on object storage.

CV is annotation-heavy and compute-heavy. Bad data infra wastes both.

What this requires

Key properties:

  • Image / video native: First-class columns.
  • Versioned annotations: Branchable; merge after QA.
  • Hybrid retrieval: Label predicate + similarity.
  • GPU-native streaming: PyTorch / JAX / TF.
  • Scales to PB: Object-storage backed.

Approaches teams try

What each gets you:

ApproachRoboflow / FiftyOneCustom S3 + JSONDeeplake ★
PB scaleLimitedYesYes
Versioned annotationsYesNoYes
Hybrid retrievalSomeNoYes
Streaming to GPULimitedDIYNative
Open sourcePartialYesYes

Reference architecture

One platform, full pipeline.

CV team
     │
     ├─► annotation tool ─► writes to Deeplake branch
     ├─► curation UI ─► hybrid query
     ├─► training (snapshot pinned)
     └─► eval (same store, slice = query)

All four read the same dataset.

Set it up

A few commands.

1. Install

bash
pip install deeplake

2. Create the dataset

bash
deeplake create deeplake://org/cv-corpus

3. Stream

bash
for batch in ds.pytorch(batch_size=64): ...

Where this usually breaks

  • Annotation tool as the data store: Caps at TBs; locks you in.
  • Custom JSON + S3: No versioning, no query.
  • Vector DB silo: Hybrid retrieval needs both predicates.
  • Tabular warehouses: Wrong shape for images and video.

FAQ

Compatible with my annotation tool?

Most tools export to S3; one-time ingest into Deeplake.

FiftyOne integration?

Yes; Deeplake datasets work alongside.

Hybrid retrieval = both?

Yes; predicates and similarity in one query.

Open source?

Yes.

Cost at PB?

Object storage cost.

Multi-cloud?

S3, GCS, Azure.

Citations

The data platform CV teams scale on

Deeplake is open source, image / video native, versioned, hybrid-queryable, and GPU-streamable.

Try Deeplake

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