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.

Artificial Intelligence & Deep Learning is transforming the entire world of technology, but AI isn't new. It has been around for decades, but AI technologies are only making headway now due to the proliferation of data and the investments being made in storage, compute and analytics technologies. Much of this progress is due to the ability of learning algorithms to spot patterns in larger and larger amounts of data. In this insideBIGDATA Guide to Artificial Intelligence & Deep Learning, we provide an in depth look at AI and deep learning in terms of how it's being used and what technological advances have made it possible. Artificial Intelligence is an amazing tool set that is helping people create exciting applications and creating new ways to service customers, cure diseases, prevent security threats, and much more.

The insideBIGDATA Guide to Deep Learning & Artificial Intelligence is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting area of technology. In this guide, we take a high-level view of AI and deep learning in terms of how it's being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We present the results of a recent insideBIGDATA survey that reflects how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains.

The insideBIGDATA Guide to Deep Learning & Artificial Intelligence is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting area of technology. In this guide, we take a high-level view of AI and deep learning in terms of how it's being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We present the results of a recent insideBIGDATA survey that reflects how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains.

The insideBIGDATA Guide to Deep Learning & Artificial Intelligence is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting area of technology. In this guide, we take a high-level view of AI and deep learning in terms of how it's being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We present the results of a recent insideBIGDATA survey that reflects how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains.

The insideBIGDATA Guide to Deep Learning & Artificial Intelligence is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting area of technology. In this guide, we take a high-level view of AI and deep learning in terms of how it's being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We present the results of a recent insideBIGDATA survey that reflects how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains.

The insideBIGDATA Guide to Deep Learning & Artificial Intelligence is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting area of technology. In this guide, we take a high-level view of AI and deep learning in terms of how it's being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We also present the results of a recent insideBIGDATA survey to see how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains. With all the quickly evolving nomenclature in the industry today, it's important to be able to differentiate between AI, machine learning and deep learning.

The insideBIGDATA Guide to Deep Learning & Artificial Intelligence is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting area of technology. In this guide, we take a high-level view of AI and deep learning in terms of how it's being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We present the results of a recent insideBIGDATA survey, "insideHPC / insideBIGDATA AI/Deep Learning Survey 2016," to see how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains.

Artificial Intelligence is transforming the entire world of technology, but AI isn't new. It has been around for decades, but AI technologies are only making headway now due to the proliferation of data and the investments being made in storage, compute and analytics technologies. Much of this progress is due to the ability of learning algorithms to spot patterns in larger and larger amounts of data. In this insideBIGDATA Guide to Artificial Intelligence, we provide an in depth look at AI and deep learning in terms of how it's being used and what technological advances have made it possible. Artificial Intelligence is an amazing tool set that is helping people create exciting applications and creating new ways to service customers, cure diseases, prevent security threats, and much more.