Artificial intelligence (AI) applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions and take action. Arun Chandrasekaran, VP Analyst, Gartner, discusses what AI architects are and what they do.
Enterprise leaders can create the role to help build a robust enterprise-wide architecture for AI.
In short, the growing diversity and urgency of Artificial Intelligence (AI) projects, products and deployment models is creating the need for an AI architect role.
AI architects envision, build, deploy and operationalise an end-to-end Machine Learning (ML) and AI pipeline. AI architects can help build a robust enterprise-wide architecture for AI and collaborate with data scientists, data engineers, developers, operations and security.
Artificial Intelligence initiatives often stall because of poor architectural choices, a lack of preparation and the inability to scale. Enterprise architecture and technology innovation leaders can create an AI architect role to help build a robust enterprise-wide architecture for AI. Through 2023, Gartner estimates that 50% of IT leaders will struggle to move their AI projects past the proof of concept (POC) stage into production. To increase the chances of success, organisations can hire an AI architect to help define the architectural strategy, create workflows, identify toolsets and scale Artificial Intelligence operations.
Who are AI architects?
AI architects are the curators and owners of the AI architecture strategy. They are the glue between data scientists, data engineers, developers, operations (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.
Work closely with security and risk leaders to foresee and overturn risks, such as training data poisoning, AI model theft and adversarial samples, ensuring ethical AI implementation and restoring trust in AI systems. Remain acquainted with upcoming regulations and map them to best practices.
What skills do AI architects need?
AI architects need a diverse set of skills that can be difficult to acquire in a short time.
Technical skills include:
AI architecture and pipeline planning.Understand the workflow and pipeline architectures of ML and deep learning workloads. An in-depth knowledge of components and architectural trade-offs involved across the data management, governance, model building, deployment and production workflows of AI is a must.
Software engineering and DevOps principles, including knowledge of DevOps workflows and tools, such as Git, containers, Kubernetes and CI/CD.
Data science and advanced analytics, including knowledge of advanced analytics tools (such as SAS, R and Python) along with applied mathematics, ML and Deep Learning frameworks (such as TensorFlow) and ML techniques (such as random forest and neural networks).
Non-technical skills include:
Thought leadership. Be change agents to help the organisation adopt an AI-driven mindset. Take a pragmatic approach to the limitations and risks of AI, and project a realistic picture in front of IT executives who provide overall digital thought leadership.
Collaborative mindset. To ensure that AI platforms deliver both business and technical requirements, seek to collaborate effectively with data scientists, data engineers, data analysts, ML engineers, other architects, business unit leaders and CxOs (technical and nontechnical personnel), and harmonize the relationships among them.
