AI in the cloud: Accessible AI services

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  Quantumrun Foresight
• November 1, 2023

Insight summary

The emergence of AI-as-a-Service (AIaaS) from cloud computing giants facilitates the development and testing of machine learning models, especially aiding smaller entities by minimizing initial infrastructure investment. This collaboration accelerates advancements in applications like deep learning. It optimizes cloud efficiency, automates manual tasks, and unveils deeper insights from data. Moreover, it's spawning new specialized job roles, influencing future work landscapes, and potentially hastening tech development in various sectors. The broader scenario indicates a democratization of machine learning technologies, intensified global competition for AI expertise, new cybersecurity challenges, and an incentive for cloud providers to invest in user-friendly machine learning platforms.

AI in the cloud context

Cloud providers, such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), want developers and data scientists to develop and test machine learning (ML) models on their clouds. This service benefits smaller companies or startups because testing prototypes often need many infrastructures, while production models often require high availability. Because cloud computing providers offer solutions to start utilizing AI technology without heavily investing in the re-haul of internal infrastructures, businesses can immediately access (and test) AI cloud services to drive their digital initiatives. Cloud computing allows for rapid and more advanced development of cutting-edge AI features, such as deep learning (DL), which has far-reaching applications. Some DL systems can make security cameras smarter by detecting patterns that may signal danger. Such technology can also identify photographic objects (object recognition). A self-driving vehicle with DL algorithms can distinguish between humans and road signs.

A study from software company Redhat discovered that 78 percent of enterprise AI/ML projects are created using hybrid cloud infrastructure, so there’s more opportunity for public clouds to attract partnerships. Various data storage choices are accessible in public clouds, including serverless databases, data warehouses, data lakes, and NoSQL databases. These options enable companies to create models near where their data is. In addition, cloud service providers offer popular ML technologies like TensorFlow and PyTorch, making them one-stop shops for data science teams who want options.

Disruptive impact

There are several ways that AI is changing the cloud and enhancing its potential. First, algorithms make cloud computing efficient by analyzing a company’s overall data storage and identifying areas that may need improvement (particularly those vulnerable to cyberattacks). Additionally, AI can automate tasks currently being done manually, freeing up time and resources for other more complex processes. AI is also making the cloud more intelligent by allowing firms to get insights from their cloud-based data that would never have been possible before. Algorithms can “learn” from information and identify patterns that humans would never be able to see. 

One of the most exciting ways that AI benefits the cloud is by creating new job opportunities. The pairing of AI and cloud computing is leading to the development of new roles that require specialized skills. For example, companies may now need employees who are experts in both areas to troubleshoot and research issues. Additionally, the increased efficiency of the cloud will likely lead to the creation of new positions focused on managing and maintaining this technology. Finally, AI is changing the cloud by heavily influencing the future of work. For example, automated tasks can lead to workers retraining for other positions. Faster and more efficient cloud computing can also enable virtual and augmented reality (VR/AR) workplaces like the Metaverse.

Implications of AI in the cloud

Wider implications of AI in the cloud may include: 

• The increasing democratization of ML technologies which will become available to small and midsized businesses that want to innovate in this space.


• Increased competition for global AI talent, which can worsen the current brain drain of AI researchers and scientists from academia to multinational businesses. The costs for recruiting and employing AI talent will also grow dramatically.


• Cybercriminals studying cloud computing services to better locate their weak points and those of companies that use such services.


• Faster development of new technologies, particularly in the autonomous vehicle and Internet of Things (IoT) sectors which require larger data and computing resources.


• Cloud computing service providers increasing their investments in no-code or low-code ML software and platforms. 

Questions to comment on

• Have you experienced any AI cloud-based service or product?


• How else do you think AIaaS will change how people work?