Definition

• 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, 15.10 MLOps Materials), and business unit leaders to govern and scale the AI initiatives. ^[1]
• They work closely with enterprise and solution architects. Still, unlike the enterprise architecture team 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?

Gartner

AI has a diverse range of use cases and deployment models, so AI architects need a wide array of capabilities:

• Collaborate 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, analysts, and IT operations — and develop 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, focusing 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.

Analytics Vidya

An AI Architect undertakes a range of tasks and responsibilities to design and implement effective AI models. Here is a list outlining the key activities of an AI Architect: ^[2]

1. Requirement Analysis: Collaborate with stakeholders to understand business needs and identify opportunities where AI can provide value and address challenges.
2. Solution Design: Develop architectural designs and system blueprints that outline the components, algorithms, and technologies required to build AI solutions.
3. Algorithm Selection: Assess various AI algorithms, models, and frameworks to determine the most suitable ones for the specific use case and problem.
4. Data Processing: Design data pipelines and strategies for acquiring, preprocessing, cleaning, and transforming data to ensure its suitability for AI model training and deployment.
5. Model Development: Oversee the development and training of AI models, selecting appropriate techniques such as machine learning, deep learning, or reinforcement learning.
6. Performance Optimization: Fine-tuning AI models and algorithms to improve accuracy, efficiency, and scalability while considering speed, memory usage, and computational resources.
7. Ethical Considerations: Ensure ethical and responsible AI practices by addressing bias, fairness, privacy, and transparency throughout the AI system’s lifecycle.
8. Integration and Deployment: Collaborate with development teams to integrate AI systems into existing infrastructure, ensuring seamless deployment, scalability, and interoperability.
9. Testing and Validation: Conduct thorough testing and validation to assess AI systems’ performance, reliability, and robustness and iteratively refine them based on feedback and evaluation.
10. Continuous Learning: Staying current with advancements in AI technologies, exploring new algorithms and methodologies, and incorporating relevant innovations into architectural design and implementation.

Skills needed

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 AI's data management, governance, model building, deployment, and production workflows is a must.
• Software engineering and DevOps principles, including knowledge of DevOps workflows and tools like 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 organization 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 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.

In short:

• The growing diversity and urgency of artificial intelligence (AI) projects, products, and deployment models creates the need for an AI architect role.
• AI architects envision, build, deploy, and operationalize an end-to-end machine learning (ML) and AI pipeline.
• AI architects can help build a robust enterprisewide architecture for AI and collaborate with data scientists, data engineers, developers, operations, and security.

References