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Learning to Rank just seems hard.
Learning to Rank just seems hard. Applying Machine Learning to relevance in Solr or Elasticsearch seems non-trivial, and it seems to require a lot of crufty code and plumbing. With Hello LTR we have come up with a series of code and notebooks that attempt to simplify the process. There might be love. There might be commitment. There might be a solid friendship at its core.
Using an open source search engine, we see our models come to life, see the pitfalls of letting machines control relevance, and work to mitigate those pitfalls. See how you can apply open source tooling to optimize your search with machine learning.
Ideal for production focused data scientists, relevance engineers, machine learning engineers, or search developers who need Learning to Rank training. Some familiarity with a search engine is expected.
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OpenSource Connections believes in empowering search teams. OpenSource Connections has worked with open source search and applied Information Retrieval since We wrote the book Relevant Search and have pioneered open source relevance tuning tools like the Elasticsearch Learning to Rank pluginQuepid and Splainer.
Your OpenSource Connections trainer is a relevance thought-leader actively working on real-life relevance issues. We expect you to bring your hardest questions to our trainers.
This training is appropriate for members of the search team that have an interest in optimizing search with advanced programming techniques and machine learning:. In this two part training course we go hands on with machine learning tools to improve relevance. Who we are OpenSource Connections believes in empowering search teams. Your trainer Your OpenSource Connections trainer is a relevance thought-leader actively working on real-life relevance issues.
We see where things can go wrong! This training is appropriate for members of the search team that have an interest in optimizing search with advanced programming techniques and machine learning: Search Engineers Data Scientists Data Engineers that use the search engine Machine learning engineers Relevance engineers Product team wanting exposure to machine learning methods.
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