Thank you,. I vote for: 1) Offline learning with the batch API 2) Low-latency prediction serving -> Online learning
In details: 1) Without ML Flink can never become the de-facto streaming engine. 2) Flink is a part of production ecosystem, and production systems require ML support. a. Offline training should be supported, because typically most of ML algorithms are for batch training. b. Model lifecycle should be supported: ETL+transformation+training+scoring+exploitation quality monitoring I understand that batch world is full of competitors, however training in batch and fast execution online can be very useful and can give Flink a edge, online learning is also desirable however with a lower priority. We migrated from Spark to Flink and we love Flink however in absence of good ML suppoer we may have to move back to Spark. -- View this message in context: http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/Machine-Learning-on-Flink-Next-steps-tp16334p16874.html Sent from the Apache Flink Mailing List archive. mailing list archive at Nabble.com.
