Artificial Intelligence (AI) has undeniably become one of the most important and fastest growing areas of investment and technological innovation in recent years. Given the large number of real-world applications of this technology, it is not surprising to find AI in common use cases: from health and life sciences to semiconductor and electronics manufacturing. chips, automotive, financial services and much more.
While generative AI tools like ChatGPT have made headlines in recent months, AI has actually been a part of our daily lives for several years. However, the latest wave of generative AI tools available to everyone is resulting in more machine-generated data than ever before, leading to an unprecedented expansion of unstructured data globally. IDC also predicts that the total volume of digital data created worldwide will amount to 175 zettabytes by 2025, compared to around 40 zettabytes in 2019. This estimate could even be revised upwards, given the current explosion of AI-generated data.
In a relatively perpetual cycle, larger volumes of data and the acceleration of AI mean for businesses not only a greater opportunity to turn that information into actionable intelligence, but also the ability to innovate faster than their competitors , increase customer satisfaction and streamline their operations, all with the aim of becoming more prosperous. However, just as oil must be refined into useful products like fuel and plastic, data must also be refined before delivering its true value. This is where data analytics (increasingly AI-based) comes into play.
How to prosper with AI projects?
In order to power AI and AI-based analytics, businesses must have a flexible, reliable, high-performance, but above all, sustainable data storage infrastructure. Performance, first of all, is key because AI relies on constantly sending massive amounts of data to GPUs. The faster companies move, the better results they achieve. AI resources, such as GPUs and data scientists, are expensive and in high demand. Delaying their access to data can therefore increase the bill. Accelerating the entire data preparation and curation business workflow is just as important as powering the GPUs. This makes data collection and processing easier from the start.
Flexibility also comes into play, as AI is arguably the most rapidly evolving area of technology: tools, techniques, datasets, and use cases are constantly evolving. It is therefore fundamental to invest in technological and infrastructure options that will allow businesses to adapt quickly to changes.
AI environments require higher levels of reliability and controls more than ever. When it comes to critical environments, any shutdown can result in exorbitant costs. Availability and reliability are therefore essential. Additionally, AI projects are often sprawling and heavily automated. Controls around quotas, security and ease of management are essential elements.
Last but not least: sustainable development, which is one of the most pressing concerns for the planet.
Why should businesses leverage AI sustainably?
According to current estimates, data centers represent 1 to 4% of global energy consumption. In certain countries, the expansion of data centers has also been stopped due to lack of access to sufficient power supply. AI will endure over time as an extremely positive tool for humanity, to automate repetitive tasks, treat diseases more effectively or even better understand the Earth’s weather and climate patterns. However, from an environmental point of view, it only worsens the problems of energy consumption and carbon footprint. Both a challenge and an opportunity, AI requires the construction of an efficient and sustainable technological infrastructure in order to limit global warming and its most serious consequences.
How can customers leverage AI sustainably?
Sustainability concerns are coming to the forefront as data volumes increase and AI’s demands for high performance become more widespread. At the same time, costs in terms of power supply, cooling and equipment hosting are soaring. In the current context of soaring energy prices, the issue is not only environmental, it is also an operational and financial challenge for companies.
Fortunately, some companies are designing products and providing services that allow customers to significantly reduce their environmental footprint. For example, all-flash storage solutions are much more efficient than mechanical disk drives (HDDs). In some cases, all-flash solutions enable data systems to consume up to 80% less direct power compared to competitive products. Additionally, flash storage is much more suitable for running AI projects.
Indeed, the key to achieving results is connecting AI models or AI-powered applications to data. To achieve this, a lot of data is needed in real time: the most crucial data must be easily accessible, across silos and applications, something that HDD-based underlying storage does not allow. On the other hand, it is a mission for which 100% flash is perfectly suited.
To further strengthen the adoption of sustainable technology options, it is recommended to rely on a sustainability manager or, failing that, someone who would be responsible for the company’s overall carbon footprint. Involving these stakeholders from the beginning of the process ensures that nothing is left to chance on the journey to sustainable AI.
Many companies are already applying these best practices to begin their AI journey. Meta, for example, wanted to help its AI specialists develop never-before-seen improved models capable of learning from trillions of examples, working in hundreds of different languages, and analyzing transparently. and simultaneous text, images and videos, to develop new augmented reality tools and much more. So the company set out to create the AI Research SuperCluster (RSC), which aims to become the world’s fastest AI supercomputer.
What are the best practices for a successful approach?
To prepare for a world in which ever-increasing volumes of unstructured data will be analyzed by AI, companies will need to equip themselves with storage systems that offer colossal space, rapid access and high durability performance.
As such, businesses should look for vendors offering a range of high-density flash storage products, suited to both the most performance-demanding workloads and those currently classified as secondary, but which will become more important with the constant rise of AI. Companies should also evaluate supplier purchasing options that can incorporate seamless capacity and technology upgrades for years to come.
Finally, companies should also look for all-flash storage providers with third-party verified ESG metrics so that their AI projects can be executed without harming the environment and their bottom line.