* ML model unification (e.g., how Google translates between many systems)
* Knowledge bases/search for both people & for algorithms. Wikis, recommendors, ... .
There are less common scenarios we see that I also expect to explode, like massively bootstrapping ML systems via weak supervision. GraphDBs & compute are also used for what I consider non-knowledge graph scenarios, likely scaling some compute tasks. Exciting times!
We primarily get pulled in as a component for projects that use as part of:
* Asset management, whether sec, IT, manufacturing specs, pharma, metadata stores, config systems...
* ML model unification (e.g., how Google translates between many systems)
* Knowledge bases/search for both people & for algorithms. Wikis, recommendors, ... .
There are less common scenarios we see that I also expect to explode, like massively bootstrapping ML systems via weak supervision. GraphDBs & compute are also used for what I consider non-knowledge graph scenarios, likely scaling some compute tasks. Exciting times!