IASA Certified IT Architect-Associate (Artificial Intelligence Architecture)

This 5-day intermediate-level programme equips professionals with the knowledge and practical capabilities to design, implement, and operationalise Artificial Intelligence (AI) solutions that support organisational strategy, product development, and digital transformation objectives. Learners will develop end-to-end skills in identifying high-value AI opportunities, designing scalable AI workflows and architectures, and integrating data, models, operations, and governance into practical AI systems.

The programme introduces modern AI architecture practices such as Large Language Model Operations (LLMOps), Retrieval-Augmented Generation (RAG), agent-based systems, AI governance, and responsible deployment. Through facilitated learning, applied team assignments, and a written assessment, learners will translate AI concepts into practical solutions that can be applied in real-world business and product environments.

The module covers the relevant IASA Body of Knowledge and provides learners with a foundation towards the IASA Certified IT Architect – Associate (Artificial Intelligence Architecture) certification. The external certification is administered by IASA and is subject to IASA’s prevailing certification assessment and requirements.

By the end of the programme, learners will be better equipped to bridge business needs with technical implementation, support AI adoption within their organisations, and contribute to roles such as AI Architect, Solutions Architect, Technical Lead, AI Engineer, Product Manager, and Digital Transformation Lead.

Recommended For

This course is designed for professionals who are involved in designing, implementing, managing, or supporting Artificial Intelligence (AI) solutions within organisational, technology, or product environments. It is suitable for individuals who need to bridge business requirements with technical implementation, and who are expected to contribute to AI adoption, solution design, and digital transformation initiatives.

The programme is relevant for roles:

AI / Solutions Architect – designing end-to-end AI systems and technical architectures
AI Engineer / Machine Learning Engineer – building and integrating AI models into applications
Data Engineer / Data Specialist – developing data pipelines and supporting AI workflows
Software Engineer (Mid–Senior Level) – integrating AI capabilities into products and platforms
Technical Lead / Engineering Manager – leading AI initiatives and overseeing implementation
Product Manager (Technical / AI-focused) – defining AI-driven product features and use cases
Business Analyst / Digital Transformation Lead – identifying opportunities to apply AI for business improvement
IT/Technology Consultants – advising organisations on AI adoption and solution design

This course is best suited for professionals who need to bridge business needs and technical execution, and who want to:

Move into AI architecture or technical leadership roles
Design practical AI solutions for real-world problems
Support or lead AI adoption within their organisations

Course Duration: 5 Days

Entry Requirements:

Learners should be able to:

Read, write and speak English at Workforce Skills Qualification (WSQ) Workplace Literacy (WPL) Level 6
Manipulate numbers at Workplace Numeracy (WPN) Level 6
Communicate effectively and able to engage in classroom discussion
Present effectively

As this is an intermediate-level programme, learners are expected to have a basic understanding of technology, digital systems, data, software, or related business processes. This will help learners engage meaningfully with the AI architecture concepts, team assignments, and applied activities covered during the course.

Certification:

Learners who have met a minimum of 75% attendance and certified competent for the assessment, and passed the IASA CITA-A in AI Architecture Exam will receive:

Certificate of Completion by IASA
Certificate of Achievement by IASA
IASA Certified IT Architect – Associate (CITA-A) Digital Badge

Learning Outcome

By the end of this course, learners will be able to identify suitable Artificial Intelligence (AI) opportunities within business processes and product environments and translate them into practical AI solution concepts. They will be able to design end-to-end AI workflows and architectures that integrate data, models, deployment considerations, governance, and operational requirements.

Learners will also be able to apply AI architecture practices such as Large Language Model Operations (LLMOps), Retrieval-Augmented Generation (RAG), agent orchestration, and AI system lifecycle management to support scalable and maintainable AI solutions. In addition, they will be able to evaluate AI solutions from the perspectives of performance, risk, governance, responsible AI, and business value.

Overall, the course enables learners to move beyond conceptual understanding of AI and apply the skills required to support AI implementation, adoption, and continuous improvement in real-world organisational settings.

Course Details

Learning Unit 1: AI Opportunities and Capabilities

Topic 1.1: AI Foundations
Topic 1.2: Prompt Engineering, Grounding, and Retrieval-Augmented
Generation (RAG)
Topic 1.3: AI Software Architecture

Learning Unit 2: AI Workflows and Governance

Topic 2.1: LLMOps Lifecycle
Topic 2.2: AI Governance

Learning Unit 3: AI Systems and Agents

Topic 3.1: AI Agents Fundamentals
Topic 3.2: Model Context Protocol (MCP) and Orchestration
Topic 3.3: Agent-to-Agent (A2A) Communication
Topic 3.4: Versioning and Lifecycle Management of AI Systems

Learning Unit 4: AI System Design and Integration

Topic 4.1: Data Management for AI
Topic 4.2: Quality Attributes
Topic 4.3: Case Study and Practical Application
Topic 4.4: End-to-End Architecture

Assessment

Team Assignment and Written Test

Registration Information and Policies

Please refer to this link Registration Policy – ATD Solution for the Registration Information and Policies.

Course Fees and Fundings

Training Facilities

Designated fully-equipped rooms to support training delivery and conduct of assessment are as follows:

Training & Assessment Room 1

1 Irving Place, #05-05, The Commerze@Irving, S369546

Training room setup in Singapore

Training & Assessment Room 2

1 Irving Place, #08-09, The Commerze@Irving, S369546

Training room setup in Singapore

FAQs

AI Architecture FAQs

Q1: Who is the AI Architecture course intended for?

A: This course targets professionals such as IT executives, directors, managers, architects, and senior technical practitioners that want to accelerate their company’s ethical and effective AI adoption.

Q2: What is the duration and mode of delivery for the AI Architecture course?

A: The AI Architecture course is typically delivered over multiple days through classroom-based sessions and mentoring online, offering a structured and immersive learning experience.

Q3: How can I register or find more information about the AI Architecture course?

A: You can register directly on the ATD Solution website via the course page. For corporate bookings, customised training, or further assistance, please contact our team directly.

For additional FAQs, please contact us for assistance.

Refund Policy

For withdrawal, please fill in the soft copy of the Course Withdrawal Form and return the completed form to train@atdsolution.com.

For withdrawal received more than 10 working days prior to the course:

0% cancellation fee will apply.

For withdrawal received less than 5 working days prior to the course:

50% cancellation fee will apply.

For withdrawal received less than 3 working days prior to the course:

70% cancellation fee will apply.

For withdrawal received within 24 hours of the course:

100% cancellation fee will apply.

Substitutes are welcomed at no additional charge. Enrolment form must be received at least 5 working days prior to the current course commencements date.

Postponement is allowed at no additional charge. The request must be received at least 5 working days before the class. Participants need to provide reasons for the postponement.