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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