PLEASE NOTE: Limited to 130 attendees!
Welcome to the next in the BD&ML Meetup series, and what we hope will be another interesting evening of presentations and networking
The agenda is listed below, followed by further details about the presentations and their presenters.
In an attempt to address the high number of no-shows we typically see at Meetups, we kindly request that you register your intent to attend this event via CVENT (http://www.cvent.com/d/ngqttk/4W).
RSVPing via this Meetup portal is NOT sufficient – only registrations made via CVENT will be admitted!
18:30 Doors open and networking
19:00 Building ML algorithms using Vertica SQL
19:45 Introducing the Seahorse project: quantifying and maximising data value
20:30 Beer, Pizza and Networking
Building ML algorithms using Vertica SQL
In last month’s Meetup I introduced Vertica’s SQL to prepare data for machine learning and predictive analytics. This follow-up talk expands on The Lab Series project to demonstrate how simple-to-use SQL function calls can be used to build regression, classification and clustering algorithms for machine learning.
A Jupyter notebook is being made freely available for those wanting to follow or become actively involved in this project.
From the early 1980s, Mark worked with Michael Stonebraker's Ingres RDBMS and then a number of column-store big data analytic technologies. In 2016, he joined HPE Big Data Platform as a Vertica Systems Engineer, and from September 2017 followed Vertica as it moved over to Micro Focus.
Mark frequently delivers talks at the London, Cambridge and Munich Big Data & Machine Learning Meetups, Vertica Forums and elsewhere, and is a regular blogger on my.Vertica.com.
Michael is the Chief Strategist for Information Archiving and eDiscovery at Micro Focus. He is a long standing practitioner and award winning researcher with over 20 years experience of machine learning and analytics. He believes that society and organisations benefit when data is properly managed and is initiating a project to promote better data management through data lifecycle management, valuation, interoperability and provenance & robustness.
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