Artificial Intelligence (AI) and Deep Learning (DL) represent some of the most demanding workloads in modern computing history as they present unique challenges to compute, storage and network resources. In this technology guide, insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads, we'll see how traditional file storage technologies and protocols like NFS restrict AI workloads of data, thus reducing the performance of applications and impeding business innovation. A state-of-the-art AI-enabled data center should work to concurrently and efficiently service the entire spectrum of activities involved in DL workflows, including data ingest, data transformation, training, inference, and model evaluation. The intended audience for this important new technology guide includes enterprise thought leaders (CIOs, director level IT, etc.), along with data scientists and data engineers who are a seeking guidance in terms of infrastructure for AI and DL in terms of specialized hardware. The emphasis of the guide is "real world" applications, workloads, and present day challenges. Optimized storage has a unique opportunity to become much more than a siloed repository for the deluge of data constantly generated in today's hyper-connected world, but rather a platform that shares and delivers data to create competitive business value.

Artificial Intelligence (AI) and Deep Learning (DL) represent some of the most demanding workloads in modern computing history as they present unique challenges to compute, storage and network resources. Traditional file storage technologies and protocols like NFS restrict AI workloads of data, thus reducing the performance of applications and impeding business innovation. A state-of-the- art AI-enabled data center should work to concurrently and efficiently service the entire spectrum of activities involved in DL workflows, including data ingest, data transformation, training, inference, and model evaluation. Optimized storage has a unique opportunity to become much more than a siloed repository for the deluge of data constantly generated in today's hyper-connected world, but rather a platform that shares and delivers data to create competitive business value. Optimized storage is designed for the needs of a broad range of problem domains including FinTech, life sciences, design, HPC, government, smart cities, media, energy, and many more.

This is almost like part two of my macOS Sierra review. I had a chance to test Apple's newest desktop operating system at the beginning of the summer, just before it was released in a public beta. The software hasn't changed much since, but a few of the headline features were missing from that earlier build; Apple said they wouldn't be available until the final version shipped in the fall. As it happens, Sierra arrives today as a free upgrade, so I'm picking up right where I left off. What follows is my full review of Sierra, though if you read my earlier preview, or have been using the software yourself, you won't find many big surprises here.

Don't call it OS X anymore, and if you do, Siri will probably correct you. Apple unveiled on Monday at its 2016 Worldwide Developers Conference that the Mac operating system is now called macOS, and the next version will feature the Siri vocal interface and digital assistant. Siri is the headline feature in what will be macOS Sierra. As had been rumored, a new Siri icon will be accessible in the Dock; clicking on it will launch a Siri window that iOS users will be familiar with. During the macOS Siri demo at WWDC, Apple VP Craig Federighi used Siri to find files and followed up the search to refine the results.