Machine learningBig Data, Budapest V3.0Gianluigi Vigano’Fouad Teban

Budapest, HU19th of May 2016

Use cases for Machine learning

– IoT

– Clickstream

– Predictive maintenance

– Data normalization

– Demand forecasting

– Cyber security

– Scoring

– Churn Analytics

– Social graph analysis

– Tickstore data cleanups

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Motivations of Machine Learning inside RDBMS

Minimize the data movement• No need to move the data out of the RDMS to other systems to train a model• Run predictions in RDBMS, where the data is

A single system for SQL analytics and machine learning

A familiar interface for existing RDBMS users to do machine learning

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Machine learning with HPE VerticaBig Data, Budapest V3.0

Gianluigi Vigano’Fouad Teban

Budapest, HU19th of May 2016

HPE Vertica: No limits, no compromises.

Supports all preferred tools

Open architecture50x-1,000x faster

Blazing fast analyticsUnlimited low-cost nodes

Massive scalability10x-30x more data per server

Optimized data storage

Private Cloud Public Cloud ApplianceSoftware Only

Purpose built for Big Data - from the very first line of code

Flexible deployment

Vertica high performance advanced analytics Real-time performance at Scale On Premise, Cloud and On

Demand Native optimized SQL on

Hadoop

IDOL augmented intelligence for human information Advanced enterprise search

and rich media analytics Analyze text, audio, image

and streaming video

Haven OnDemand APIs and Services Machine Learning as a

Service Delivered on Microsoft Azure

Cloud Accessible to any developer

HPE IDOL