The Open Analytics Blog
Using AI/Deep Learning to prevent retail inventory loss
Retail inventory loss (or “shrinkage”) is a serious problem, totaling about $100 billion annually—almost 1.8% of sales—worldwide. The issue is even more acute for those moving high-dollar goods, such as fashion and accessories. As traditional retailers grapple with ongoing market concentration, loss of market share to online sellers, and other pressures, there’s good news: Advanced
Megh demos Spark-based real-time streaming video analytics using FPGAs
Megh computing demonstrated “Spark-based real-time streaming video analytics using FPGAs” at the Intel booth during the Spark+AI Summit in April 2019. Key features of the demo included: Streaming analytics at scale with Spark Streaming and Analytics Zoo Real-time performance with Intel FPGA analytics pipelines acceleration Enabled by Megh Computing for various use cases with low
New white paper explores using FPGAs to accelerate real-time analytics of streaming data
Provides an overview on overcoming the limits of data center infrastructure and the challenges of FPGA deployment Portland, OR (April 18, 2019)—Megh Computing, Inc. today released a white paper for enterprise strategy and IT personnel titled “Using FPGAs for advanced real-time analytics of streaming data,” providing an overview of problems of and solutions to extracting
Implementing a real-time, deep learning pipeline with Spark Streaming
With the current information age defining the third wave, we are facing an explosion of real-time data, which is in turn increasing demand for real-time analytics. A real-time analytics solution pipeline typically utilizes a streaming library and an analytics platform. Apache Spark is an open-source, distributed computing platform designed to run analytics payloads on a