Hey everyone,
Some of you may have seen Mikhail and I talk at Spark/Hadoop Summits about MLeap and how you can use it to build production services from your Spark-trained ML pipelines. MLeap is an open-source technology that allows Data Scientists and Engineers to deploy Spark-trained ML Pipelines and Models to a scoring engine instantly. The MLeap execution engine has no dependencies on a Spark context and the serialization format is entirely based on Protobuf 3 and JSON. The recent 0.5.0 release provides serialization and inference support for close to 100% of Spark transformers (we don’t yet support ALS and LDA). MLeap is open-source, take a look at our Github page: https://github.com/combust/mleap Or join the conversation on Gitter: https://gitter.im/combust/mleap We have a set of documentation to help get you started here: http://mleap-docs.combust.ml/ We even have a set of demos, for training ML Pipelines and linear, logistic and random forest models: https://github.com/combust/mleap-demo Check out our latest MLeap-serving Docker image, which allows you to expose a REST interface to your Spark ML pipeline models: http://mleap-docs.combust.ml/mleap-serving/ Several companies are using MLeap in production and even more are currently evaluating it. Take a look and tell us what you think! We hope to talk with you soon and welcome feedback/suggestions! Sincerely, Hollin and Mikhail
