Across the autonomous vehicle industry, compression is no longer optional. Datasets are growing faster than ML pipelines can absorb, creating compounding pressure on storage, data transfer, pipeline throughput and development timelines.

Many teams are already compressing video data or evaluating how to. But compression introduces a critical question the industry has not rigorously answered: How do you confirm that compressed video preserves ML model integrity across every scenario and pipeline stage?

ADAS & Autonomous Vehicle International magazine featured Beamr's ML-safe compression framework, benchmarked across the AV pipeline.

The differentiator is compression with proof. Programs operating without a demonstrated framework are making infrastructure bets they cannot verify, accumulating data assets the integrity of which they cannot confirm. For teams managing tens or hundreds of petabytes, the infrastructure case is straightforward once the integrity question is settled. Storage, egress, I/O time and pipeline throughput all improve with compression gains.

-> Read the full article in ADAS & Autonomous Vehicle International magazine