Jake Mannix

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Experienced search / recommender-systems / distributed systems architect and team lead with fifteen years of software engineering experience, currently specializing in distributed machine learning and search-relevance algorithm development as applied to personalized search and content recommendations. Specialties: distributed faceted real-time search, distributed computing, parallelizing algorithms (but hopefully not paralyzing them!), applied machine learning, classification as a service, social network search, lucene, solr, hadoop, spark, nosql, java, scala, algebraic topology, quantum field theory, inflationary cosmology

search machine learning personalization deep learning data science spark data engineering solr #spark #solr #machinelearning #lucidworks graph computation big data mahout hadoop linear algebra collaborative filtering

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Jake Mannix

Presentations

Seattle Scalability Mahout

Practical Machine Learning for Smarter Search with Solr and Spark

Practical Machine Learning for Smarter Search with Spark+Solr

Deep Learning for Search: Personalization and Deep Tokenization