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How R Powers Data Science at Microsoft

In this video “How R Powers Data Science at Microsoft” from the EARL 2017 conference in San Francisco (June 5-7, 2017), insideBIGDATA’s Managing Editor and resident data scientist Daniel D. Gutierrez chats with Vijay K. Narayanan – Director, Algorithms and Data Science Solutions, Microsoft.

Big Data Project Failure Pain Points and their Solution

Big data projects don’t typically fail for a single reason, and certainly not for technology alone. A combination of factors serve to derail big data deployments. Problems and failures occur due to factors including business strategy, people, culture, inattention to analytics details or the nuances of implemented tools, all intensified by the rapid advancement of digital transformation.

Building a Machine Learning Platform at Quora

In this MLconf SF 2016 talk “Building a Machine Learning Platform at Quora,” Nikhil Garg, Engineering Manager at Quora, discusses the core ideas behind his company’s machine learning platform, as well as some of the specific systems, tools, and abstractions that have enabled the company to scale their approach to machine learning.

Book Review: The Mathematical Corporation by Josh Sullivan and Angela Zutavern

As a data scientist, I know first hand how today’s enterprise has some catching up to do with engaging the mathematical foundations for capitalizing on an ever-increasing volume of data assets. This is why a new title is so important: “The Mathematical Corporation – Where Machine Intelligence and Human Ingenuity Achieve the Impossible,” by Josh Sullivan and Angela Zutavern. Sullivan and Zutavern are, respectively, senior vice president and vice president of Booz Allen Hamilton.

Want to Run Cassandra in a Container? You Need to Solve These 4 Problems First

In this contributed article, Gou Rao, Co-founder and CTO at Portworx, discusses how running the open source distributed NoSQL database Cassandra in a container requires some special considerations. If you can solve these problems, you’ll have gotten most of the way to a successful Cassandra deployment in containers.

Go for Big Data

Many of the hottest modern infrastructure projects are powered by the Go programming language, including Kubernetes, Docker, Consul, etcd, and many more. Go is turning into a go to language for devops, web servers, and microservices. One route to providing robust, performant, and scalable data processing within Go is to utilize the Intel® Data Analytics Acceleration Library (Intel® DAAL) within Go programs.

Introducing Flo – Bringing Deep Learning to Video Editing

Everyone loves to shoot videos but don’t know what to do with them. Editing videos is difficult, time consuming and a skillful job. NexGear Technology has built Flo – an intelligent camera and movie maker app that auto-edits raw video footage into short cinematic movies using deep learning and artificial intelligence. Flo combines machine learning, computer vision, and natural language processing to put together the interesting moments from your videos into a short cinematic movie.

Big Data – A Game Changer in the Music Industry

In this contributed article, content contributor Ravindra Savaram discusses the ways that big data is becoming transformative for the music industry. For a long time, the music industry is in need of a facelift and the advent of big data seems to fulfill this. The musicians might be provided with a more successful revenue model with the emergence of big data.

AI vision system PIXEVIA Recognizes Cars and People on Video in Real-time for Smart Cities

Well functioning traffic flows, real-time parking availability, increased security and swift crime solving, cost-efficient defect detection in the infrastructure or updates on road conditions – smart cities could already be taking advantage of real-time fully automated video surveillance from drones, ground vehicles with video cameras and CCTV, Lithuanian startup PIXEVIA claims.

Do Security Professionals Really Need Big Data?

In this contributed article, tech writer Linda Gimmeson discusses two basic approaches to securing big data: staying small and strategic, or leveraging big data for a broader approach. Either approach can be successful, but it is up to the organization and security professionals to strategically decide how much big data they actually need.