Machine Learning for Big Data Analytics: Scaling In with Containers while Scaling Out on Clusters

Armed with nothing more than an Apache Spark toting laptop, you have all the trappings required to prototype the application of Machine Learning against your data-science needs. From programmability in Scala, Java or Python, to built-in support for Machine Learning via MLlib, Spark is an exceedingly effective enabler that allows you to rapidly produce results.

Of course, as soon as your prototyping proves successful, you’ll want to scale out to embrace the volume, variety and velocity that characterizes today’s Big Data demands … in production. Because Spark is as comfortable on an isolated laptop as it is in a distributed-computing environment, addressing Big Data requirements in production boils down to effectively and efficiently embracing containers and clusters for Big Data Analytics.

And this is where offerings from Univa shine – i.e., in making the transition from prototype to production completely seamless. For some use cases, it makes sense to scale-in Spark based applications within Docker containers via Univa Grid Engine Container Edition; whereas in others, Spark is interfaced (as a Mesos-compliant framework) with Univa Universal Resource Broker, to permit scaling out on a cluster. In both scenarios, your production Spark applications are scheduled alongside other classes of workload – without a need for dedicated resources.

And that’s just the beginning … To learn more, please join us for this webinar.

Interested, but can’t attend? Register anyway and we’ll send you the recording.

Register at: http://bit.ly/29YNsnQ