ayw

At Scale (scale.com), we strongly believe that the “open-source” alternative to this is pretty critical.

We’ve built this index for autonomous driving datasets (https://scale.com/open-datasets) and are building that out for other domains right now.

Open source data has been a pillar to progress in ML (starting with ImageNet). It should continue to be the case that data that enables researches is sufficiently democratized.

Strongly agree with this.

One thing I’ll mention is that this is true both at the very early stages of a ML project, and even when an ML project is scaled up and in production. Oftentimes, the data pipeline is the true way in which a model will improve versus anything else, so it’s pretty critical that these data pipelines are setup to get an initial dataset but also to scale properly.

It’s one reason I started Scale (scale.com). It was viscerally clear that the real bottleneck to ML was getting the needed data, and in our case, annotating that data appropriately. It is very heartening to hear it echoed in this whole thread that data is very clearly what “matters” for ML.