Alibaba Cloud’s Dr Li Feifei: Combining AI & Cloud Computing

We speak with Dr Li Feifei, President of Database Products Business at Alibaba Cloud, about the benefits of combining AI with serverless cloud computing

Given the rapid acceleration of AI tools like OpenAI’s ChatGPT, increasing numbers of businesses are seeking to invest in or harness generative AI (Gen AI) to some extent. 

However, generating AI may be easier than we think, according to Dr Li Feifei, President of Database Products Business, Alibaba Cloud.

Technology Magazine speaks with Dr Li about how AI can be combined with serverless cloud computing so that businesses can better manage their data. He explains how, given that data is the foundation for AI, businesses must properly process and protect it in order to create effective AI.

When it comes to AI development, why is data important?

Dr Li explains that more recently, these conversations have been developing into how businesses can make AI work better, especially when it comes to high costs and increased complexities for businesses.

“The technology is promising because it can be wise,” he says. “It can spot things we as humans may miss or even disregard because it has more capacity and consistency. But fundamentally, data is the foundation for AI.”

He continues: “We need to ensure data is properly processed and protected. It is not just the lifeblood of the entire IT infrastructure, but it is also the basis of all innovation that comes from real humans or AI. As part of the underlying infrastructure that powers Gen AI, databases have evolved to cater to the demands from corporations in the generative AI era. 

“How effective your AI is, boils down to how you are managing data and using the right database.

What are some common database models?

“One type of database is an Online Transaction Processing (OLTP), which supports online transaction processing. Basically, this enables businesses to transact concurrently – for example, during online banking, shopping, and so on – and, as data accumulates in the database, you can derive value out of the data pool,” Dr Li highlights.

“We also have On-Line Analytical Processing (OLAP) – which enables organisations to conduct fast, interactive, and powerful analytics from the data because it helps consolidate data from multiple sources beyond just the transactional ones. 

“For example, a retailer can combine data in its inventory and what it has in stock with another dataset of what customers are buying to provide intelligence on the need to increase production of a particular item over another due to higher demand.

He continues: “Meanwhile, another database model family - called NoSQL - is popular because it helps sort unstructured data, unlike the two above.”

How can AI help to better manage databases within an enterprise?

In the emerging age of AI, in line with big data, Dr Li expects that the vector database model will be the most transformative. This is because the model is used to handling intelligent workloads with large language models (LLMs) for storing high-dimensional vectors.

He says: “Think of unstructured data, such as documents, images, audio recordings, videos and so on, which is on track to account for over 80% of all data worldwide by 2050 – to enable the semantics expected of AI, which is understanding the underlying context and nuances, rather than just the meaning.

“AI is ultimately about making sense of the data, and you can’t make sense without using a vector database. It is a key requirement to increase the industry-specific knowledge of large language models, which is one of the largest constraints facing generative AI models.”

Dr Li also notes that AI is able to manage databases itself, by altering staff when storage is running low, or if there is a need to extend storage space. He says that these functions can also be applied to CPU and memory capacity.

“The promise of AI is not just limited to gaming or even making sense of unstructured data,” he says. “This capability is handy given the move towards serverless cloud computing. As the name suggests, serverless means that there is no longer a need to worry about servers behind the service you're using.”

He continues: “In the past when one purchased a cloud service product, a provision had to be made for a set of servers. For example – four core eight gigabytes of memory – but that came at a cost. When one provisions a server that has more capacity than the actual workloads require, server resources are wasted.”

Alibaba Cloud is already helping enterprise customers to harness the power of Gen AI, having recently launched a new serverless cost-efficient solution for AI model deployment and inference to both individuals and enterprises. The product is designed to make it easier for businesses to build their own customised Gen AI applications.

Ultimately, why should businesses combine AI with serverless cloud computing?

Dr Li explains that serverless computing is designed to address the challenges of AI, stating that server capacity can be used by cloud services to match workload needs. However, he also highlights that, if workloads are changing dynamically over time, serverless solutions may end up costing a business more.

“By combining AI with serverless cloud computing, we are really getting the best of both worlds,” he explains. “That’s why some companies are making their key AI-driven database products serverless. Customers pay only for the number of resources required and AI is there to guide and augment decision-making capabilities in dealing with sudden peaks in demand or a very dynamic workload.”

He adds: “How you make AI work for you by working with the right databases, will determine whether your organisation rides this AI trend towards success or is left behind.”

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