( DevOps , DataOps , MLOps ) and business unit leaders to govern and scale the AI initiatives .
They work closely with enterprise and solution architects , but unlike the enterprise architecture team , which is responsible for a broad set of functions , they are laser-focused on building a robust enterprise-wide architecture for AI .
What do AI architects do ?
AI has a diverse range of use cases and deployment models , so AI architects need a wide array of capabilities . These Include :
• Collaboration with data scientists and other AI professionals to augment digital transformation efforts by identifying and piloting use cases . Discuss the feasibility of use cases along with architectural design with business teams and translate the vision of business leaders into realistic technical implementation . At the same time , bring attention to misaligned initiatives and impractical use cases .
• Align technical implementation with existing and future requirements by gathering inputs from multiple stakeholders – business users , data scientists , security professionals , data engineers and analysts , and those in IT operations – and developing processes and products based on the inputs .
• Play a key role in defining the AI architecture and selecting appropriate technologies from a pool of open-source and commercial offerings . Select cloud , on-premises or hybrid deployment models , and ensure new tools are well-integrated with existing data management and analytics tools .
• Audit AI tools and practices across data , models and software engineering with a focus on continuous improvement . Ensure a feedback mechanism to assess AI services , support model recalibration and retrain models .
AI architects need a diverse set of skills that can be difficult to acquire in a short time .
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