3 ms·
I'm not sure that is true. I think inference speed is often the bottleneck for the use cases stated, as is the need for frequent re-training. As a result algori
by usgroup 3y ago
I'm not sure that is true. I think inference speed is often the bottleneck for the use cases stated, as is the need for frequent re-training. As a result algorithms like catboost are very popular in those domains. I think catboost was actually invented by Yandex.
PS: Its weird that you are being down-voted. I think your opinion is reasonable.
- Scene_Cast2 3y agoInference speed: more sophisticated stacks use multiple stages. Early stage might be a sublinear vector search, and the heavy hitting neural nets only rerank the remainder. Bytedance has a paper on their fairly fancy sublinear approach. Retraining - online training solves this for the most part. Frameworks - the only battle-tested batteries-included one I've seen is Vespa. Noone else publishes any of interesting bits. KDD is the most relevant conference if you're interested in the field. IIRC Xiaohongshu has some papers that can only really be done with NNs.