AI applications often benefit from fundamentally different architectures than those used by traditional enterprise apps. And vendors are turning somersaults to provide these new components. This complimentary document comprehensively details the elements of a strategic IT plan that are common across the board – from identifying technology gaps and risks to allocating IT resources and capabilities. You forgot to provide an Email Address. This email address doesn't appear to be valid.
Enterprises are increasingly having to deal with growing data volumes, fuelling demand for advanced analytics and machine learning tools to help them make sense of it all. This, in turn, has placed new requirements on IT infrastructure to cope with the computational demands of such techniques. It is now a decade or so since the notion of "big data" became a hot topic among CIOs and business decision-makers in enterprise IT, but many companies are still struggling to implement a successful strategy to make full use of the data they have, and become more insight-driven. The data springs from numerous sources, such as machine-generated or sensor data from the internet of things (IoT) and other embedded systems, transactional data from enterprise systems, or data from social media and websites. As a result, enterprise workloads are evolving beyond traditional ones that revolve around structured datasets and transaction processing, and are starting to incorporate analytics and other techniques, such as artificial intelligence (AI).
Sponsored Artificial intelligence and machine learning hold out the promise of enabling businesses to work smarter and faster, by improving and streamlining operations or offering firms the chance to gain a competitive advantage over their rivals. But where is best to host such applications – in the cloud, or locally, at the edge? Despite all the hype, it is early days for the technologies that we loosely label "AI", and many organisations lack the expertise and resources to really take advantage of it. Machine learning and deep learning often require teams of experts, for example, as well as access to large data sets for training, and specialised infrastructure with a considerable amount of processing power. This is because cloud service providers have a wealth of development tools and other resources readily available such as pre-trained deep neural networks for voice, text, image, and translation processing, according to Moor Insights & Strategy Senior Analyst Karl Freund.
Businesses are increasingly using data assets to accelerate their competitiveness and drive greater revenue. Part of this strategy is to use machine learning and AI tools and technologies. But AI workloads have significantly different data storage and computing needs than generic workloads. AI and machine learning workloads require huge amounts of data both to build and train the models and to keep them running. When it comes to storage for these workloads, high-performance and long-term data storage are the most important concerns.