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From Hollin Wilkins <hol...@combust.ml>
Subject [ML] MLeap: Deploy Spark ML Pipelines w/o SparkContext
Date Thu, 02 Feb 2017 16:42:10 GMT
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

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