The Open Analytics Blog
Megh’s platform strategy
The demand for real-time stream processing is increasing rapidly with the explosion of data from the Web, sensors, IoT and mobile devices, and other sources. Enterprises want to process this data as it moves to create business value in areas such as: Finance (e.g., fraud detection, risk management) Sales and marketing (e.g., customer preferences) Operations
Megh’s flexible, high-performance deep learning engine
As deep learning (DL) becomes more pervasive, the need for efficient and fast computation is increasing. Traditional central processing units (CPUs) and graphical processing units (GPUs) are typically used for acceleration, despite the limiting nature of their fixed architectures. Field programmable gate arrays (FPGAs) have been highlighted for their flexibility, but until recently have fallen
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