AI-ready technology architecture

Most of us have experienced serverless architecture as a way to build and run applications and services without having to manage infrastructure. One of the key advantages of serverless technology is its ability to handle dynamic workloads. AI applications often require processing large volumes of data, and serverless platforms can automatically scale to meet these demands.

Large language models (LLMs) have revolutionized how we interact with machines, enabling tasks such as text generation, translation, and question answering. However, these features come at a cost, as LLMs require high amounts of computational power both for training and inference. Transformer models, which LLMs are built on, have simultaneously increased in size since their inception and the trend seems to continue due to the clear performance benefit. With widespread adoption of LLMs thus comes concerns about environmental impact, contradictory to most companies’ sustainability agendas to reach the SBTi targets. 

The potential of AI assistants like Microsoft Copilot and ChatGPT to revolutionize workplace productivity is undeniable. A ubiquitous personal assistant seamlessly integrated into workflows, offering real-time suggestions, automating time-consuming tasks, and extracting key information from complex documents will drive efficiency and effectiveness. Early adopters within organizations report significant individual gains, but widespread adoption will still face significant challenge.

The exponential growth of AI applications open doors to countless opportunities, but it also presents a critical challenge: balancing the power of data-driven insights with the fundamental right to data privacy. Users increasingly prioritize control over their information, while regulations like GDPR and CCPA demand rigorous data protection measures. This complex intersection creates a need for innovative approaches that reconcile user preferences, regulatory compliance, and the need for efficient AI development. Federated machine learning, differential privacy, edge computing and hybrid infrastructure help us navigate these complexities.