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Growth in Machine Learning: Accessibility of MLaaS and the Future of the Industry

In this special guest feature, Mateusz Czajka, Head of Technology and Product Design at Netguru, discusses how Machine Learning as a Service, or MLaaS, is completely breaking down barriers and has the potential to completely change the way we do business and make decisions –and it’s here now!

Best of arXiv.org for AI, Machine Learning, and Deep Learning – July 2019

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

Develop Multiplatform Computer Vision Solutions with Intel® Distribution of OpenVINO™ Toolkit

Realize your computer vision deployment needs on Intel® platforms—from smart cameras and video surveillance to robotics, transportation, and much more. The Intel® Distribution of OpenVINO™ Toolkit (includes the Intel® Deep Learning Deployment Toolkit) allows for the development of deep learning inference solutions for multiple platforms.

XAIN Puts AI Privacy First, at No Cost to Efficiency, with its Distributed AI Solution

XAIN, the AI startup that specializes in privacy-oriented Federated Machine Learning (FedML), is developing an infrastructure to train artificial intelligence applications through FedML technology, a mechanism that emphasizes data privacy. XAIN’s distributed approach to machine learning, which intends to comply with the European Commission’s General Data Protection Regulations (GDPR), also provides greater efficiency in the way data is trained, marking a major breakthrough in a field otherwise burdened by costly and onerous processes.

Field Report: KDD 2019

As a very long time member of the ACM and their SIGKDD group, I’d always wanted to attend a KDD conference (first one occurred in 1995). This year I received a gracious invitation to attend KDD2019 in Anchorage, Alaska, August 4-8. It satisfied two of my bucket list items: witnessing a KDD first-hand and also […]

TOP 10 insideBIGDATA Articles for July 2019

In this continuing regular feature, we give all our valued readers a monthly heads-up for the top 10 most viewed articles appearing on insideBIGDATA. Over the past several months, we’ve heard from many of our followers that this feature will enable them to catch up with important news and features flowing across our many channels.

Optimizing Fuel Pricing in a Convenience Retail Environment with AI and Machine Learning

In this special guest feature, Niels Skov, SVP, PDI Fuel Pricing Solutions, outlines how fuel pricing is a complex business for convenience retailers. Using advanced digital capabilities like AI and machine learning to get fuel pricing right can have a significant business impact far beyond an operator’s raw margins on gasoline or diesel.

Data Centers Get a Performance Boost From FPGAs

With the advent of next generation workloads, such as Big Data and streaming analytics, Artificial Intelligence (AI), Internet of Things (IoT), genomics, and network security, CPUs are seeing different data types, mixtures of file sizes, and new algorithms with different processing requirements. Hewlett Packard Enterprise’s Bill Mannel explores how as big data continues to explode, data centers are benefitting from a relatively new type of offload accelerator: FPGAs.

Help! My Data Scientists Can’t Write (Production) Code!

In this contributed article, Nisha Talagala, Co-founder and CTO/VP of Engineering at ParallelM, takes a hard look at productionizing machine learning code and how integrating SDLC practices with MLOps (production ML) practices certifies that all code, ML or not, is managed, tracked and executed safely.

Why You Need a Modern Infrastructure to Accelerate AI and ML Workloads

Recent years have seen a boom in the generation of data from a variety of sources: connected devices, IoT, analytics, healthcare, smartphones, and much more. This data management problem is particularly acute in the areas of Artificial Intelligence (AI) and Machine Learning (ML) workloads. This guest article from WekaIO highlights why focusing on optimizing infrastructure can spur machine learning workloads and AI success.