David Linthicum, a thought leader in AI architecture, discusses important aspects of AI architecture with Thomas Erl, covering topics like AI system integration, clean data importance, API management, model explainability, and trust in cloud automation features for data hygiene.
Generative AI is still early in the cloud, led by Microsoft, Google, and AWS.
AI architecture includes data management, model development, and model management for effective integration.
Deep dives
The Current State of AI in the Cloud
The current state of AI in the cloud, particularly generative AI, is described as still early days despite the incredible capabilities available. Companies like Microsoft lead in generative AI developments, with others like Google and AWS also investing in their tools to catch up. While there is a lot of hype around generative AI, actual implementation and investment in transformative systems are predicted to be a few years away due to challenges like data training and system integration.
Technology Architecture of AI Systems
The technology architecture of AI systems revolves around three main components: data management, model development, and model management/usage. Data management involves sourcing and cleansing diverse data for training, ensuring data quality is critical for effective model building. Model development requires collaboration between data scientists and AI engineers to design, build, train, and deploy AI models, with crucial decisions on toolsets and system customization. Model management/usage focuses on administering APIs and prompts that interact with the AI system, emphasizing consistency, security, and governance to enable business system integration.
Explainability and Model Evolvement
Explainable AI tools are essential to understand and govern complex AI models, especially as the systems continuously learn and evolve. There is a potential risk of AI behavior changing over time, leading to inconsistent results as the system gains more knowledge. Ensuring integrity, transparency, and accountability in AI systems is critical to address biases, monitor model behavior, and explain reasoning behind decisions, mitigating potential legal and ethical implications.
As AI becomes an increasingly prominent part of the IT mainstream and as organizations are faced with integrating AI systems with their IT enterprises, there is a growing need for IT professionals to understand the technology and architecture that underlies AI implementations. Thought leader David Linthicum discussses with Thomas Erl the important aspects of AI architecture, and how to best prepare for AI adoption.