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Interview: David Steinmetz, Machine Learning Engineer at Capital One

I recently caught up with Daniel Steinmetz, who is a Machine Learning Engineer with Capital One Bank to discuss how to get a job at Capital One, the types skills they are looking for, and what his typical day looks like.

The Importance of Vectorization Resurfaces

Vectorization offers potential speedups in codes with significant array-based computations—speedups that amplify the improved performance obtained through higher-level, parallel computations using threads and distributed execution on clusters. Key features for vectorization include tunable array sizes to reflect various processor cache and instruction capabilities and stride-1 accesses within inner loops.

“Above the Trend Line” – Your Industry Rumor Central for 9/18/2017

Above the Trend Line: your industry rumor central is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items grouped by category such as people movements, funding news, financial results, industry alignments, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

How To Spend Less Time Processing Queries And More Time Gaining Insights

In this contributed article, Richard Heyns, CEO of Brytlyt discusses the trend of companies adopting GPU database software and the things they should keep in mind when adopting a GPU database.

Identifying Health Risks Using Pattern Recognition and AI

Physicians are increasingly using AI technologies to treat patients with superhuman speed and performance, and predictive analytics will be key to delivering more effective, proactive, and quality care. Stephen Wheat, Director of HPC Pursuits at Hewlett Packard Enterprise, explores how we can identify health risks using pattern recognition and AI. 

Julia: A High-Level Language for Supercomputing and Big Data

Julia is a new language for technical computing that is meant to address the problem of language environments not designed to run efficiently on large compute clusters. It reads like Python or Octave, but performs as well as C. It has built-in primitives for multi-threading and distributed computing, allowing applications to scale to millions of cores. In addition to HPC, Julia is also gaining traction in the data science community.

The Next Generation of Managing Enterprise Data: Cloud Optimized

The inherent elasticity and agility of a cloud-based data platform provides a major operational and financial advantage. This is the final article in a three-part series from Podium. This post focuses on the growing importance of being cloud optimized for managing enterprise data.

“Above the Trend Line” – Your Industry Rumor Central for 9/4/2017

Above the Trend Line: your industry rumor central is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items grouped by category such as people movements, funding news, financial results, industry alignments, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

From the Editor’s Bookshelf: My Favorite Titles for Data Science and Machine Learning

As a practicing data scientist, I’ve spent years building up my library of academic and practical resources that I routinely draw upon for helping me do my work. Although my library is vast, I have a select group of books that occupy a prominent position on my desk. I’ve been asked enough times about my “favorite titles” list, I thought I’d write this article for my readers.

Intel AI Lounge – Bryce Olson, Global Marketing Director, Health and Life Sciences at Intel

In this Silicon Angle video interview, Bryce Olson, Intel Global Marketing Director, Health and Life Sciences and stage 4 cancer survivor, set out to find better treatment for his illness through technology. By using AI to sift through his DNA data, he is now in remission.