Almost a third (32%) said that deep learning would'totally' transform their industry, while 26% said other kinds of machine learning would have this effect. Most CTOs highlight machine learning as the technology that will disrupt their industry and lead to new innovations. But, how can they effectively put machine learning models into production? However, only 60% said they were confident that they knew what deep learning is and how it works, despite participants having direct responsibility for overseeing AI. The sample was made up of 350 CIOs and senior AI decision makers in the UK and the Nordics, all of whom worked for organisations of at least 1,000 employees.

Phase 2: Experiment: We help customers test the waters with A.I. by applying pre-determined experiments to a companys unique problems both within our Centers of Excellence, designed to assist IT departments and data scientists who are looking to accelerate their deep learning applications and realize better ROI from their deep learning deployments in the near term, and eventually within a company. With HPE Pointnext, scaling deep learning and A.I. is as easy as turning the dial for more capacity, but without losing control of the underlying architecture. We offer customers a flexible consumption services for HPE infrastructure, which avoids over-provisioning, increases cost savings and scales up and down as needed to accommodate the needs of deep learning deployments.

Bengaluru: Hewlett Packard Enterprise (HPE) today announced new purpose-built platforms and services capabilities to help companies simplify the adoption of Artificial Intelligence, with an initial focus on a key subset of AI known as deep learning. Inspired by the human brain, deep learning is typically implemented for challenging tasks such as image and facial recognition, image classification and voice recognition. To take advantage of deep learning, enterprises need a high performance compute infrastructure to build and train learning models that can manage large volumes of data to recognize patterns in audio, images, videos, text and sensor data. Many organizations lack several integral requirements to implement deep learning, including expertise and resources; sophisticated and tailored hardware and software infrastructure; and the integration capabilities required to assimilate different pieces of hardware and software to scale AI systems. To help customers overcome these challenges and realize the potential of AI, HPE is announcing the following offerings: • HPE's Rapid Software Development for AI: HPE introduced an integrated hardware and software solution, purpose-built for high performance computing and deep learning applications.

HPE announced new purpose-built platforms and services capabilities to help companies simplify the adoption of Artificial Intelligence, with an initial focus on a key subset of AI known as deep learning. Inspired by the human brain, deep learning is typically implemented for challenging tasks such as image and facial recognition, image classification and voice recognition. To take advantage of deep learning, enterprises need a high performance compute infrastructure to build and train learning models that can manage large volumes of data to recognize patterns in audio, images, videos, text and sensor data. Many organizations lack several integral requirements to implement deep learning, including expertise and resources; sophisticated and tailored hardware and software infrastructure; and the integration capabilities required to assimilate different pieces of hardware and software to scale AI systems. Based on the HPE Apollo 6500 system in collaboration with Bright Computing to enable rapid deep learning application development, this solution includes pre-configured deep learning software frameworks, libraries, automated software updates and cluster management optimized for deep learning and supports NVIDIA Tesla V100 GPUs.