Creating Analytical Models to Achieve Better Results
When it comes to the creative processes inherent in predictive modeling, it is time for a new paradigm in which the user and machine learning work in tandem to achieve better results than could be achieved working separately. See, through real use cases, how companies are automatically creating analytical models to advance medical discovery, gain more insights in IOT environments, and improve the accuracy of failure prediction in preventative maintenance.
As the Practice Manager for Big Data, Patrick Toole brings over 15 years of experience in systems engineering and leadership in delivering, architecting, and implementing MPP and software solutions for RoundTower Technologies ("RTT"). Patrick spent 2 years with MapR, where he was responsible for integrating and bringing to market partnerships for MapR. As the Senior Director of Business Development, he resurrected relationships between MapR and SAS, which was dwindling. At present, SAS is supporting MapR on SAS?s entire suite of software. Patrick helped MapR staff a FTE on site at SAS to maintain this relationship. Patrick was also responsible for MapR?s relationship with Oracle, Hewlett Packard, and CenturyLink.
Before MapR, Patrick managed the System Engineering team at Hadapt. Patrick took an emerging database-on-Hadoop technology and positioned it for success. By creating a repeatable GTM strategy that included machine learning algorithms, Patrick set Hadapt up for success, which was later acquired by TeraData. Prior to Hadapt, Patrick spent his time as one of the top System Engineers for Vertica, one of the top MPP database solutions. At the time Patrick joined Vertica, the database market was a closed book, dominated by Oracle, Microsoft, and Sybase. Patrick helped seek out and land major accounts that became Vertica?s hallmark calling card: Verizon and Zynga. Vertica was eventually acquired by HP and is now their flagship product of the HaVEN initiative.
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