AI-Ready Data Infrastructure: Transforming Industries

AI is going from strength to strength and is beginning to spur transformative change in industries from finance to healthcare to education and beyond.

A sort of rhythm has been established—a leading player announces the latest feat of its AI model, and no sooner have we retrieved our jaws from the floor, we are presented with another announcement, this time of feats more impressive still.

But beneath the public spectacle lies an unseen foundation of data. Large language models are, above all, large, and more recent multi-modal AI models are even larger. They rely on enormous quantities of high-quality data. And this is where AI-ready data infrastructure comes into play.

The old tactic of simply stacking computing power has given way to a more comprehensive approach that integrates computing, storage, and transport capabilities, to form a foundation for high-quality data mining, advanced storage, and seamless data flow. Therefore, AI-ready data infrastructure that can offer high performance, resilience, and reliable data access will be key to bolstering large AI models in the coming era.

Why is AI-ready data infrastructure so crucial for organizations and enterprises aiming for digital and intelligent transformation and innovation?

Better data management: Most enterprises are facing challenges such as dealing with data of varying quality from different sources. This makes it difficult to quickly prepare and manage the large amounts of data needed to train large AI models. AI-ready data infrastructure supports global data management, eliminating data silos and significantly accelerating the entire AI service process.

Increased computing power utilization: The number of parameters of large AI models has been increasing exponentially. However, most computing power is wasted — most GPUs sit idle most of the time. Therefore, building AI-ready data infrastructure instead of just stacking GPU computing power is essential, and will help enterprises increase the efficiency of checkpoint recovery, optimize cluster utilization, and minimize computing power waste.

Unleashing industry potential: The applications of AI are vast, spanning a diverse range of industries. From improving risk management in finance to revolutionizing healthcare with AI-powered diagnosis, AI-ready data infrastructure will serve as a platform for the intelligent transformation of industries, as they reach for new heights of innovation and efficiency.

Let's take a closer look at three industries where Huawei AI-ready data infrastructure is beginning to enable exciting changes:


1.    Finance: a leap in efficiency and risk management

AI has proven its worth in four critical areas of banking:

1. enhancing financial product marketing
2. improving risk management capabilities
3. boosting operational efficiency
4. enabling new service forms

However, the industry faces challenges such as data silos, real-time processing needs, and stringent regulatory requirements. AI-ready data infrastructure can address these issues by providing seamless data integration and real-time analytics capabilities.

It can also enable financial institutions to process vast amounts of data quickly and accurately, leading to better decision-making and risk management, through capabilities such as detecting fraud in real time and offering customized financial advice based on customers' spending patterns.

Related: Huawei Digital and Intelligent Services: Ensuring Financial Data Center Resilience


2.    Healthcare: a stride toward intelligent public health



The healthcare industry plays a pivotal role in improving public health and safeguarding people's well-being. However, it grapples with challenges such as scarcity of medical resources and lack of diagnostic capabilities.

AI-powered healthcare applications can effectively tackle these issues, making the industry more digitalized and intelligent. AI-ready data infrastructure is crucial for managing the vast amounts of healthcare data, in multiple formats, collected and generated by those applications, facilitating real-time analysis and decision support across the healthcare ecosystem.

3.    Education: revolutionizing research and learning


The education industry is witnessing a surge in scientific research data, as technology allows the collection of more data than ever before, necessitating the adoption of AI-ready data infrastructure to manage and analyze various types of data in real time. By providing real-time analytics and insights, AI-ready data infrastructure empowers educators and researchers to make data-driven decisions, fostering innovation in research.

There is no AI without sufficient data. The quality and quantity of data determine how far AI will go, making AI-ready data infrastructure a critical component of any AI endeavor. As industries continue to embrace AI, reliable and scalable AI-ready data infrastructure is of paramount importance. By providing the necessary computing power, storage capacity, and data accessibility, it empowers organizations and enterprises to leverage AI for innovation, efficiency, and ultimately, a better future.

As AI increasingly makes its presence felt in our world, the importance of the underlying data infrastructure will only grow, with far-reaching implications for every industry.

Download Huawei's AI-Ready Data Infrastructure Reference Architecture White Paper.

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Disclaimer: Any views and/or opinions expressed in this post by individual authors or contributors are their personal views and/or opinions and do not necessarily reflect the views and/or opinions of Huawei Technologies.