Alibaba Cloud addresses skills gap challenge in AI

“The demand for AI talent far exceeds supply, making AI skills development a national priority. Alibaba Cloud actively supports this initiative via student development, educator support, and startups,” says Kun Huang, General Manager of Malaysia at Alibaba Cloud Intelligence.

Alibaba Cloud recently announced its inaugural Alibaba Cloud Malaysia AI Hackathon 2025 during its AI Tech Day in Kuala Lumpur. The hackathon, which was held in partnership Malaysia’s digital solutions specialist Agmo Holdings Berhad during the event, invites innovators, developers, and tech enthusiasts in the region to collaborate, create, and showcase their AI expertise.

While the hackathon aims at accelerating local AI innovation and talent development in the region, the reality is Malaysian organizations are still struggling to close the skills gap challenge in AI. In fact, the skills gap in AI, cybersecurity, data centers and other tech fields continue to be addressed through partnerships with vendors in the country.

At the event, Alibaba Cloud also announced the global rollout of its 9th Generation Enterprise Elastic Compute Service (ECS) instances, set to be available in mid of 2025 in Malaysia. The latest generation of ECS instances has notable performance enhancements compared to its previous iteration, including a 20% increase in computing efficiency. Additionally, by accelerating networks through eRDMA (elastic Remote Direct Memory Access), its performance in supporting high-performance computing, search recommendations, and Redis databases can be further improved by up to 50%.

Developing AI talent

For Alibaba, the hackathon is just one of the many programs it has launched in Malaysia to address this issue.

“The demand for AI talent far exceeds supply, making AI skills development a national priority. Alibaba Cloud actively supports this initiative via student development, educator support, and startups,” said Kun Huang, General Manager of Malaysia at Alibaba Cloud Intelligence.

To date, the Chinese cloud service provider has trained over 20,000 Malaysians in cloud skills through programs like the Alibaba Cloud Academic Empowerment Program (AAEP) and Alibaba Cloud’s Digital Heroes Program. The AAEP provides academic institutions with access to Alibaba Cloud learning resources, aiming to nurture future talent in cloud computing, data intelligence, and cloud security.

It has also collaborated with Universiti Malaya to launch the Alibaba Cloud Academy Skills Centre and the first local internship program. The Skills Centre offers training sessions and workshops on topics like AI and cloud computing.

Additionally, Huang mentioned that the Alibaba Cloud Certified Associate (ACA) Generative AI Engineer Course, which is available globally since May 2024, is part of their commitment to supporting digital talent development and making AI more accessible.

Apart from that, Alibaba Cloud announced the digital Accelerator program to boost AI adoption by grooming 50 independent software vendors (ISVs) by equipping them with essential cloud knowledge, particularly in GenAI, last year. By building such an AI-driven ecosystem, Alibaba Cloud together with the ISVs aims to assist and onboard 1,000 small and medium-sized enterprises (SMEs) on their digitalisation journey this year.

“Together with local partners from enterprise and academia, we are committed to building a sustainable and inclusive ecosystem to nurture digital talents and pave ways for digital transformation for the nation,” said Kuang.

The state of AI in Malaysia

As AI is projected to contribute approximately US$115 billion to Malaysia’s economy, Kuang believes this contribution to the national GDP demonstrates AI’s potential across a variety of sectors; especially like manufacturing, healthcare, finance, and public services among them which we are currently working closely together.

“Cloud computing plays a pivotal role in enabling AI, making it more accessible, cost-effective, and scalable, especially for businesses looking to integrate AI without heavy upfront investments,” added Kuang.

For Kuang, businesses can take a practical and phased approach to AI adoption by focusing on four key areas: business needs, technical infrastructure, talent development, and ecosystem collaboration.

“From a business perspective, companies should start by identifying inefficiencies where AI can add value. Common use cases include Smart Assistants for customer interactions, automated customer service, marketing automation, and personalised product or service recommendations. By targeting specific pain points, businesses can implement AI in manageable steps rather than attempting large-scale overhauls,” explained Kuang.

On the technical side, Kuang pointed out that a strong data infrastructure is essential for AI success. Organizations should build robust data pipelines and unified platforms to streamline AI integration. Scalable cloud-based AI services can help companies experiment with AI without requiring heavy upfront infrastructure investments, making it easier to scale as they grow.

From a talent perspective, Kuang mentioned that businesses must invest in AI literacy and upskilling. Training employees to work effectively with AI ensures that teams can adopt and manage AI solutions efficiently. Collaborating with educational platforms and industry experts can accelerate this learning curve.

Finally, Kuang believes that ecosystem collaboration plays a crucial role in successful AI adoption. Partnering with industry peers, software providers, and AI specialists can provide businesses with tailored AI solutions suited to their industry needs. This approach enables organisations to leverage existing expertise and proven AI models, reducing complexity and cost while ensuring effective implementation.

“By starting with specific use cases, strengthening data capabilities, upskilling teams, and collaborating within the AI ecosystem, businesses can adopt AI in a scalable and cost-effective way, unlocking long-term value,” said Kuang.

Interestingly, Kuang also believes that open-source models will lead to the democratization of AI, making AI applications based on smaller parameter models proliferate.

“The future of AI innovation will be driven by companies that focus on reducing training and inference costs, aligning technological scaling with economic realities. Cloud computing underpins these trends by providing scalable infrastructure, cost-efficient resource pooling, and seamless collaboration tools, enabling businesses to experiment, scale, and secure AI solutions without heavy upfront investment,” he concluded.