seldon-server

els built with TensorFlow, Keras, Vowpal Wabbit, XGBoost, Gensim and any other model-building tool — it even supports models built with commercial tools and services where the model is exportable. Fork Seldon on Github: https://github.com/SeldonIO/seldon-server/ Seldon Deploy - in development - a machine learning deployment platform for data science teams. Features include workflows with approvals and audit trails, advanced experiments, continuous integration and model explanations. Seldon was part of the Barclays Techstars '16 and we’re working with customers in banking, technology and media sectors. Disclosure: I’m CEO at Seldon.

to recommendations. # Item Activity Correlation Built for static slowly changing historical inventory - Similar to Amazon’s “people who bought this also bought…” - Use historical user activity to find items that share similar user activity. - Apache Spark scalable offline implementation. - Upload for each item: top-N similar items. - For each user: item recommendations based on their historical activity. # Topic Models Built for sites needing long tail recommendation - Assume activity is associated with a set of topics. - Users individuals tastes are covered by a subset of topics. - Describe users by the set of keywords for the items they have interacted with. - Built with Apache Spark and Vowpal Wabbit implementation of Latent Dirichlet Allocation. - Online serving layer scores user association with items in real time. # Latent Factor Models Best for e-commerce sites lower churn sites - Netflix Prize-winning solution. - Use Matrix Factorization to reduce activity matrix to two low dimension user and item factor matrices. - Load factors into API servers and score users and items in real time. - Fold-in new users and items until next batch update of model. - Utilize Apache Spark mllib and streaming modules. # Content Similarity Built for services with rich metadata and high sparsity - Requirement – fast content based technique to match user history to similar content based on text/tags of content. - Utilize random vectors technique. Each word/tag is assigned a random high-dimensional vector. - Open-source Semantic-Vectors and word2vec implementations. - Periodically process recent content into vectors and update servers. - Servers load vectors into memory. - Recommendation on recent user activity to find similar content in real-time. (via http://seldon.io/algorithms)