In the past decade, the rise of artificial intelligence (AI) and machine learning (ML) has transformed how businesses handle data in almost every business operation and industry. In fact, as of a year ago, 37% of organisations have implemented AI in some form, which is a 270% increase over the last four years. The ability to harvest data for insight has sparked a new digital revolution, and the more data we create, the more vital the data centre becomes.
In order to effectively implement large-scale data collection and analysis across an entire business, organisations must build unified infrastructure that is devoted to AI projects. This article will outline some defining trends as the rise of AI continues to shape the data centre.
The rise of embedded intelligence
As devices become increasingly more connected with each other, the reality is that we are moving toward an era of embedded intelligence. It is therefore crucial that devices within the same ecosystem are able to communicate with each other and access data in the cloud, at the edge and in the core.
An example of this is the development of autonomous vehicles that need a common “language” to communicate, regardless of the car manufacturer. The safety of autonomous driving is dependent on an ecosystem of smart traffic signals, roadside units and pedestrian alerts, which is why standardisation and interoperability are key. Ultimately, this will make an AI/ML ecosystem much easier to integrate and deploy at the edge.
“Touch it twice? Automate it”
More than 59 zettabytes (ZB) of data will be created, captured, copied, and consumed in the world this year, according to IDC. For IT teams to keep afloat amid this explosion of data and the complexity of diverse workloads, automation is essential for success.
On one hand, automation is a way of relieving the pressure from IT staff and freeing their time to more important projects. However, automation is also key in helping AI take on more functions in the data centre by removing tasks that rely on close human interaction. Automation is what will help data centres make the journey to AI and move from being reactive to preventative, and ultimately, predictive.
Increasing efficiency with AI chips
Using general-purpose chips for large-scale AI processes can be outrageously expensive. As AI demands such enormous computational power, many businesses opt for specialised AI chips, which are designed to perform complex mathematical and computational tasks in a more efficient way. With most AI use cases today being very narrow, AI chips can be configured for a specific task such as pattern recognition, natural language processing, network security, robotics, and/or automation.
As AI continues to mature, capabilities will not only expand but the cost of implementation will also decrease. This trend will advance even further with the RISC-V initiative, and other open-source technologies lowering the barriers to purpose-built “building blocks” that can focus on efficiency, performance, and scalability like never before.
Bridging the skills gap
The data science skills gap, along with the exponential growth of machine learning workloads, means that there simply are not enough data scientists in the world to manage this explosion in data.
To help bridge this gap and reduce the need for human input, AI technology is being made more accessible to people with other skill-sets across the business. By expanding existing tools and building a self-service platform, people with no prior training will be able to generate predictive, AI-based analysis. To some extent, anyone in an organisation should be able to fulfil the baseline role of the data scientist.
The future of data centres
As data grows and applications become more complex and diverse, the data centre is desperate for efficiency improvement. Some go as far to say that without AI, many data centres will not be economically or operationally viable. Some of the ways AI tools will assist is by improving resource and service provisioning, cooling and power optimisation, and by detecting more cyber threats.
The technology behind AI brings data to life, and it is critical that the wider data centre can support these processes efficiently. As AI continues to develop and data growth accelerates, many businesses are taking advantage of embedded intelligence, customised AI chips and automation to continue improving the data analytics process. Ultimately, the most successful data centres will strategically deploy AI capabilities across most operations, as well as establishing a smarter, highly efficient and flexible infrastructure.
Forward-Looking Statements:
This article may contain forward-looking statements, including statements relating to expectations for Western Digital’s the market for Western Digital’s storage products, potential uses for these products, and future capabilities and technologies for those products. These forward-looking statements are subject to risks and uncertainties that could cause actual results to differ materially from those expressed in the forward-looking statements, including development challenges or delays, supply chain and logistics issues, changes in markets, demand, global economic conditions and other risks and uncertainties listed in Western Digital Corporation’s most recent quarterly and annual reports filed with the Securities and Exchange Commission, to which your attention is directed. Readers are cautioned not to place undue reliance on these forward-looking statements and we undertake no obligation to update these forward-looking statements to reflect subsequent events or circumstances.
Davide Villa – EMEA Business Development Director, Western Digital
