Vector search is where the next leap in search and recommender system improvements will come from. * Is already coming from. Google, Bing, Spotify, Pinterest,

Vector search is where the next leap in search and recommender system improvements will come from. * Is already coming from. Google, Bing, Spotify, Pinterest, Facebook, etc, already use it in production. All flavors of ES have some kind of vector search available, albeit with some limitations. There are also stand-alone open-source and managed solutions popping up left and right (I work for one, Pinecone.io). From what I’m seeing, the hard parts people encounter are: - Finding or developing good embedding models. I consider KPI testing a part of this process, since it tells you whether or not your models are actually “good.” - Finding and tuning vector search libraries that can meet speed and accuracy requirements. (Easy if you use ES although you’ll run into latency issues with >10M docs. Even easier with a managed vector search solution.) - Scaling the above to handle millions (or billions!) of items and high query throughput. Again, less of an issue if you’re using managed ES on AWS or Elastic.co, or a managed vector search solution like Pinecone, or you have a team of distributed systems engineers sitting idle.