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Mon, 23 Jan 2017 13:09:35 -0800 (PST) MIME-Version: 1.0 Sender: nivanov@gridgain.com Received: by 10.103.88.3 with HTTP; Mon, 23 Jan 2017 13:08:52 -0800 (PST) From: Nikita Ivanov Date: Mon, 23 Jan 2017 13:08:52 -0800 X-Google-Sender-Auth: P_9MVCI1cti2hE5GL9ZuruWTxBM Message-ID: Subject: Adding ML to Ignite, IGNITE-4572 To: dev@ignite.apache.org Content-Type: multipart/alternative; boundary=001a1143e542aa993d0546c96a2a archived-at: Mon, 23 Jan 2017 21:09:50 -0000 --001a1143e542aa993d0546c96a2a Content-Type: text/plain; charset=UTF-8 Igniters, I'd like to kick off the conversation on adding ML capabilities to Ignite as it relates to IGNITE-4572. I started off some preliminary steps on this ticket and would like to invite anyone interesting in implementing distributed core algebra on top of Ignite to join me in this work. There's plenty to contribute from ideas and math, to implementation, tests, and documentation. Background on IGNITE-4572. ---------------------------------------- Throughout many conferences last year I have heard the sentiment that one of the functional areas that is badly missing in Ignite is a library of distributed Machine Learning algorithms. In fact, it was frequently noted as the only reason not to finally migrate from Spark to Ignite... ML, in general, is a pretty large undertaking and there are more than a few ways to approach this in the context of Ignite. One idea I'd like to chew on and prototype is to look close at Apache Mahout ( http://mahout.apache.org/). After a deep look at Mahout codebase I don't believe the outright code-level integration is feasible at this points - but we can certainly borrow as many ideas from it as possible. Thanks! -- Nikita Ivanov --001a1143e542aa993d0546c96a2a--