It has never been easier to build AI or machine learning-based systems than it is today. The ubiquity of cutting edge open-source tools such as TensorFlow, Torch, and Spark, coupled with the availability of massive amounts of computation power through AWS, Google Cloud, or other cloud providers, means that you can train cutting-edge models from your laptop over an afternoon coffee.
Though not at the forefront of the AI hype train, the unsung hero of the AI revolution is data?—?lots and lots of labeled and annotated data, curated with the elbow grease of great research groups and companies who recognize that the democratization of data is a necessary step towards accelerating AI.
However, most products involving machine learning or AI rely heavily on proprietary datasets that are often not released, as this provides implicit defensibility.
With that said, it can be hard to piece through what public datasets are useful to look at, which are viable for a proof of concept, and what datasets can be useful as a potential product or feature validation step before you collect your own proprietary data.
It’s important to remember that good performance on data set doesn’t guarantee a machine learning system will perform well in real product scenarios. Most people in AI forget that the hardest part of building a new AI solution or product is not the AI or algorithms?—?it’s the data collection and labeling. Standard datasets can be used as validation or a good starting point for building a more tailored solution.
This week, a few machine learning experts and I were talking about all this. To make your life easier, we’ve collected an (opinionated) list of some open datasets that you can’t afford not to know about in the AI world.
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