Background Video streaming is predicted to account for a staggering 82 percent of the total internet traffic by 2022. From basic android mobile to 4K premium tv – viewers are watching content on devices with varied resolutions, compute & capability. The massive amount of data streaming presents challenges to broadcasters […]
Background Artificial intelligence (AI), with machine learning (ML) and deep learning (DL) technologies, promises to transform every aspect of businesses, devices, and their usage. In some cases, it enhances the current process, and in some cases replaces them. As video streaming devices (including TV &[…]
Background In recent years, deep learning-based Artificial Intelligence (AI) and Machine Learning (ML) models are moving from cloud to the edge devices for various factors such as bandwidth as in Figure-1. Power consumption, latency, hardware size are important aspects for inference at the edge. Moreover, The Models developed in the cloud[…]
Background Retail is going through a massive transformation in multiple fronts. Online Stores are racing ahead with several advantages compared to Offline stores. The data collected by the online stores about the buyers is becoming a huge advantage to target products and to come up with merchandise that is likely to address[…]
Background This blog outlines the steps involved and things to care about for transfer learning a propensity model. Sales team members could be inundated with several prospects. The best use of their time is by lead scoring and focusing on the prospects that have best chance to close. A Propensity-To-Buy model helps[…]
Background Data is the new Oil. Companies should treasure data. The new class of powerful Machine Learning (ML) and Artificial Intelligence (AI) algorithms are data hungry and are as good as the data provided to them. Several useful (profitable) insights can be derived from data across the organization benefiting different[…]
Background This blog outlines the advantages of compute attached to storage and reference architecture to implement it in cloud with FPGAs. Almost all the deep learning algorithms are very memory intensive and it takes more energy (power) to get data into and out of the CPU/GPU than the compute itself. Optimal sche[…]
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