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To someone not familiar with the space, search seems like an incredibly complex and difficult space to get right. In your view, is it reasonable for the average

To someone not familiar with the space, search seems like an incredibly complex and difficult space to get right. In your view, is it reasonable for the average developer prepared to read both of those books to expect to come out the other side and construct something ready for production? Thanks!

tallytarik 3y ago
IMO: yes. I've used both of these in my current role to make substantial improvements to our Solr search engine. They include a good range of techniques between "quick wins you could implement and test in an hour" and "complex machine learning pipelines based on millions of data points". AI Powered Search was probably the more interesting and useful but it's also a bit of a misnomer. Half of the techniques aren't related to AI (which is fine) and the half that are, are rapidly out of date. Semantic/vector search is now miles ahead of what the book talks about, with dense vector support in Solr/Elastic/Opensearch; sparse models; hybrid search/RRF... but I digress :) If you're interested in how to improve the magic black box that is search, they're worthwhile reads. Just remember that expectations are everything. There are no two books, or twenty books, that'll turn your out-of-the-box Solr instance into Google or Bing quality. But you can end up with a magic black box that serves much better results, which is nice!
WinLychee 3y ago
It's a problem with a long tail, and it very much depends on what objective you're optimizing for. In search at least, you aim for "good" and "better", but will never achieve "perfect". It's a pretty interesting space at the meeting point of software and data science. You probably don't necessarily need to read full books before diving in, but play around with "learning to rank" https://xgboost.readthedocs.io/en/latest/tutorials/learning_to_rank.html https://xgboost.readthedocs.io/en/latest/tutorials/learning_... and maybe check out https://www.microsoft.com/en-us/research/uploads/prod/2017/06/INR-061-Mitra-neuralir-intro.pdf https://www.microsoft.com/en-us/research/uploads/prod/2017/0... . Also https://www.tensorflow.org/recommenders/examples/basic_retrieval https://www.tensorflow.org/recommenders/examples/basic_retri... .

tallytarik 3y ago

IMO: yes. I've used both of these in my current role to make substantial improvements to our Solr search engine. They include a good range of techniques between "quick wins you could implement and test in an hour" and "complex machine learning pipelines based on millions of data points". AI Powered Search was probably the more interesting and useful but it's also a bit of a misnomer. Half of the techniques aren't related to AI (which is fine) and the half that are, are rapidly out of date. Semantic/vector search is now miles ahead of what the book talks about, with dense vector support in Solr/Elastic/Opensearch; sparse models; hybrid search/RRF... but I digress :) If you're interested in how to improve the magic black box that is search, they're worthwhile reads. Just remember that expectations are everything. There are no two books, or twenty books, that'll turn your out-of-the-box Solr instance into Google or Bing quality. But you can end up with a magic black box that serves much better results, which is nice!

WinLychee 3y ago

It's a problem with a long tail, and it very much depends on what objective you're optimizing for. In search at least, you aim for "good" and "better", but will never achieve "perfect". It's a pretty interesting space at the meeting point of software and data science. You probably don't necessarily need to read full books before diving in, but play around with "learning to rank" https://xgboost.readthedocs.io/en/latest/tutorials/learning_to_rank.html https://xgboost.readthedocs.io/en/latest/tutorials/learning_... and maybe check out https://www.microsoft.com/en-us/research/uploads/prod/2017/06/INR-061-Mitra-neuralir-intro.pdf https://www.microsoft.com/en-us/research/uploads/prod/2017/0... . Also https://www.tensorflow.org/recommenders/examples/basic_retrieval https://www.tensorflow.org/recommenders/examples/basic_retri... .