The creation and processing of large amounts of data -- coupled with expanding innovations, such as artificial intelligence and machine learning -- present many opportunities for organizations to make better use of their data and find trends to inform business decisions. But there are also many challenges with AI storage architecture, including planning for large data stores and heavy compute needs. Planning storage for AI can be a difficult task. Chinmay Arankalle, senior data engineer at Energy Exemplar and co-author of The Artificial Intelligence Infrastructure Workshop, hopes the book will bring clarity on how to implement AI and storage for AI. The authors wrote that, when designing an AI system, organizations should consider storage requirements upfront to fit the type of analysis they intend to perform.

Machine learning infrastructure is probably the greatest thing to focus on when building machine learning models. Creating processes for integrating machine learning within a company's current computational infrastructure stays a challenge for which robust industry standards don't yet exist. However, organizations are progressively understanding that the advancement of an infrastructure that underpins the consistent training, testing, and deployment of models at an enterprise scale is as essential to long-term viability as the models themselves. Small organizations, notwithstanding, the battle to go up against enormous companies that have the assets to fill the huge, modular teams and processes of internal tool development that are regularly important to create strong ML pipelines. At present data scientists, who should focus on significant AI advancement, need to do loads of DevOps work before they are prepared to do the thing they do best: playing with the data and algorithms.

Data analytics has rarely been more newsworthy. Throughout the Covid-19 coronavirus pandemic, governments and bodies such as the World Health Organization (WHO) have produced a stream of statistics and mathematical models. Businesses have run models to test post-lockdown scenarios, planners have looked at traffic flows and public transport journeys, and firms use artificial intelligence (AI) to reduce the workload for hard-pressed customer services teams and to handle record demand for e-commerce. Even before Covid-19, industry analysts at Gartner pointed out that expansion of digital business would "result in the unprecedented growth of unstructured data within the enterprise in the next few years". Advanced analytics needs powerful computing to turn data into insights.

Artificial Intelligence, Machine Learning and High Velocity Analytic workloads are going mainstream. Enterprises of all types and sizes want to seize the opportunity their data presents. As these workloads move from development to production, organizations face a significant challenge with the supporting storage architecture. At the heart of the problem is the file system the organization will use to store the information. It needs to be fast, scalable, durable and cloud-ready.

Artificial Intelligence (AI) is a broad term that can apply to various computing tasks, including machine learning, deep learning, and big data analytics. Many AI projects are in a proof of concept stage, but CIOs and IT Managers need to understand that in the future, almost every business outcome and workflow will use and depend upon some form of AI processing. The time is now to prepare the Infrastructure for that eventuality. As AI environments move into production and begin to grow in size and importance, organizations need a strategy to address challenges the AI at scale will create for both the compute and storage architectures. For the last decade, developing a cloud strategy was at the top of every CIO's to-do list.

Artificial intelligence initiatives are springing up in almost every industry and generating a huge market in their wake. Gartner predicts that AI augmentation will generate $3.9 trillion in business value by 2022 alone. What's more, Gartner says that AI promises to be the most disruptive class of technologies during the next 10 years, driven by increases in computational power, advances in storage technology, the availability of new data and the ubiquity of deep learning toolkits. Organizations making the journey to AI face a multitude of complex choices related to data, skillsets, software stacks, analytic toolkits and infrastructure components. Each of these choices has significant implications for the time to value associated with AI initiatives.

If IT were a television show, it would be "Hoarders." Organizations are creating and storing more and more data every day, and they're having a difficulty finding effective places to put it all. In fact, according to research by IDC, by 2020 we will hit the 44 zettabyte mark, with about 80 percent of the data not in databases. With such unprecedented data growth, IT teams are looking for flexible, scalable, easily manageable ways to preserve and protect that data. This is where object storage shines.