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Deep Lake Methodology

Deep Learning Analytics for the Responsive 21st Century Enterprise

 
 
 

Ingest, Integrate, Insight, Invest, Iterate

 

Alluviate Deep Lake Methodology provides an end-to-end suite that employs Deep Learning Analytics on a Data Lake.   The Methodology is based on formal Information Theoretic foundations,  this leads to robust and reliable actionable insights from existing data.   Alluviate provides state-of-the-art solutions by synergistically combining the disruptive capabilities of Big Data Hadoop, Spark, TensorFlow, Intel Many Integrated Core and High Performance Computing technologies.  

 

Software and Hardware

Deep Learning Analytics Suite (DLAS)

Data Inventory and Discovery

Deep Learning Analytics Lifecycle management

Scalable Spark and TensorFlow based Deep Learning

Xeon Phi optimized HPC platform

 

 

 

Deep Question and Answer (DQnA)

Automate codification of enterprise knowledge

Leveraging Deep Learning Analytics

 Query Unstructured Data from Learned Features

User friendly Natural Language Queries 

 

 

 

 

Wetware

Carlos Perez

Software Architect Deep Learning

MS Comp. Sci,  Univ. of Massachusetts

Co-Founder

 

Kevin Bates

Enterprise Big Data

MS Computational Chemistry, Rice U

Advisor

 

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Thomas Pe

Financial Risk Developer

Ph.D. Condensed Matter, Iowa State U

Advisor

Albert de Vera

Big Data DevOps

BS Physics, Univ. of Philippines 

Co-founder

 

Rab Boyce

IT Service Management Methodology 

B.S. Software Eng., Univ. West Scotland

Advisor

 

Paco Sandejas

Venture Capital

Ph.D. Electrical Eng.,  Stanford U

Advisor

Josephine Palencia

High Performance Computing

MS Non-Linear Dynamics, Drexel

Advisor

 

Drew Perez

Intelligence Analytics

Masters Strategic Intelligence, AMA

Advisor

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Monetize and Leverage Your Existing Data