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Machine Learning Managed Services: Can Big Tech Provide Viable Alternatives to IIoT Predictive Maintenance Software?

In this contributed article, Deddy Lavid, CTO of Presenso, offers 5 important questions to ask when considering machine learning managed services, specifically can new technology provide viable alternatives to IIoT predictive maintenance software.

Accelerate Business Value with Serverless Computing

In order to answer questions about serverless computing, Continuous Data Platform Iguazio offers a whitepaper “Accelerate Business Value with Serverless Computing” that weighs the pros and cons of function-as-a-service, or serverless computing in the cloud. The report defines exactly what serverless means (newsflash: it’s a misnomer), examining the offerings and challenges of the technology, and giving software engineers a look at how they can weigh their options when deciding how to engage with the hottest new areas in cloud computing.

DDN and Parabricks Announce Solution for GPU Acceleration of Genome Analysis

DDN Storage (DDN®) and Parabricks announced the availability of a jointly-integrated technology solution that provides massive acceleration for analysis of human genomes. The breakthrough platform combines GPU supercomputing performance with DDN’s Parallel Flash Data Platforms for fastest time to results, and enables unprecedented capabilities for high-throughput genomics analysis pipelines.

Atomic X Unveils ‘Rufus’, An Intelligent AI-Powered Chatbot

Atomic X, an experiential artificial intelligence (AI) firm and consulting business, has announced its emergence into the global AI marketplace with the launch of its flagship product, Rufus, the most advanced bot on the market to introduce complex chatbot automation combined with a traditional live-chat system.

ArrayFire Releases v3.6 Announced

ArrayFire announces the release of ArrayFire v3.6, our open source library of parallel computing functions supporting CUDA, OpenCL, and CPU devices. This new version of ArrayFire includes several new features that improve the performance and usability for applications in machine learning, computer vision, signal processing, statistics, finance, and more.

Periscope Data Introduces Visual Data Discovery for Business

Periscope Data, the software platform built to address the complete analytics lifecycle, introduced the Spring 2018 release of its platform, including new visual data discovery capabilities designed to empower business professionals to explore trusted data sets on their own within the Periscope Data platform.

The Future of AI and Education

In this contributed article, freelance human Avery Phillips discusses the practical uses of AI in the education industry as well as an assessment of AI’s role in current and future educational activities. Education professionals are taking on the task to implement AI into operations, and finding it to be quite beneficial.

Deep Learning Course Student Launches Big Data Visualization Software Company

Richard Sheng is the co-founder of QuantumViz, a big data visualization software company that allows data scientists and analysts to find insights in massive data sets, and create amazing data stories in 3D, VR, or AR. Richard worked in data science previous to taking the Deep Learning course with NYC Data Science Academy but now works as the CEO and co-founder of QuantumViz, which was his final project of the course.

The $24 Trillion Industry That’s Fashionably Late to the Big Data Party

In this contributed article, Min Suh, CEO and Founder of Assess+RE, discusses how as decisions are made faster, and are informed by a much wider range of highly accurate, timely data, we’ll see the benefits of a more robust and responsive CRE industry spread to just about every other business sector in the world. Despite the industry’s stubbornness, CRE is finally ready to utilize the power of big data analytics.

Data-Efficient Machine Learning

From Quadrant (a D-Wave business), this whitepaper “Data-Efficient Machine Learning” describes a practical impediment to the application of deep neural network models when large training data sets are unavailable. Encouragingly however, it is shown that recent machine learning advances make it possible to obtain the benefits of deep neural networks by making more efficient use of training data that most practitioners do have.