Is computational time for predictions on the order of few milliseconds (< 10 ms) like the old mllib library?
On Thu, Feb 2, 2017 at 10:12 PM, Hollin Wilkins <hol...@combust.ml> wrote: > 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 >
