Build a strong technical foundation in AI concepts, lifecycle, and leadership context.

1. AI Essentials (Build)

• Core AI/ML concepts, terminology & capabilities
• Types of AI: predictive, generative, agentic
• AI ecosystem, tools & platforms overview
• Key risks and technical limitations

2. AI Life Cycle (Build)

• Data collection, preparation & model development
• Training, fine-tuning & evaluation
• Deployment, monitoring & continuous improvement
• MLOps fundamentals

3. AI Leadership (Build)

• Leading AI engineering teams & cross-functional delivery
• Technical decision-making & stakeholder communication
• Prioritization: build vs. buy vs. fine-tune
• Engineering culture for responsible AI delivery

4. AI Governance (Build)

• Governance touchpoints in the engineering workflow
• ISO 42001, EU AI Act & NIST AI RMF — engineer's view
• Model documentation, versioning & audit trails
• Privacy, security & compliance by design

Design AI architectures and hands-on build working agents.

5. AI Tech Stack & Architecture (Engineer)

• LLM providers, APIs & model selection
• Vector databases, RAG pipelines & embeddings
• Orchestration frameworks (LangChain, LlamaIndex, etc.)
• Reference architectures for enterprise AI systems

6. AI Use Cases (Engineer)

• Mapping business problems to AI solution patterns
• Industry use case deep dives (support, ops, engineering, analytics)
• Build-vs-buy decision framework with real cases
• ROI and feasibility assessment

7. Agentic Engineering & Agents (Engineer)

• Agent architecture: memory, tools, planning & reasoning
• Single-agent vs. multi-agent systems
• Human-in-the-loop vs. autonomous design patterns
• Hands-on lab: building a working AI agent end-to-end

Scale agents and AI systems into resilient, governed production environments.

8. Scaling Tech (Scale)

• From prototype agent to production system
• Load, latency, cost & reliability at scale
• Observability, guardrails & rollback strategies
• Hands-on lab: scaling your agent — deployment & monitoring

Frame problems for AI-enabled products and workflows, prioritize use cases by feasibility and value, and architect AI services, assistants, copilots, and domain applications.

Work across the major AI ecosystems — OpenAI and Codex, Anthropic Claude and Claude Code, Google AI and Vertex AI, Microsoft Azure AI and Copilot, AWS Bedrock and SageMaker, the NVIDIA AI stack, and Hugging Face — for development, orchestration, and deployment.

Build prompt-driven application patterns, RAG architectures using vector stores and retrieval design, and agentic workflows with tool use and function-calling, using frameworks such as LangChain and LlamaIndex.

Prepare structured and unstructured data for model-driven applications, including cleansing, labeling, chunking, embeddings, indexing, and privacy controls.

Establish deployment pipelines, version and rollback management, and monitoring for quality, latency, token usage, and operational anomalies.

Apply secure handling of prompts, credentials, and sensitive enterprise data, and build governance guardrails for model choice, access control, and auditability.

CAIP is a professional-track, applied-delivery certification from Digital Union, focused on building and deploying AI solutions with practical delivery capability and responsible implementation.

AI practitioners, solution architects, consultants, technical leads, product teams, and professionals responsible for building or implementing AI-enabled services.

You will learn to frame AI problems and select use cases, prepare data and evaluate models, design AI architectures and RAG pipelines, build working AI agents, and scale AI systems into governed production environments — across the Build, Engineer, and Scale phases.

Basic knowledge of programming and AI/ML fundamentals, along with completion of a pre-class technical assignment sent by the facilitator.

The CAIP course is delivered over 4 days plus hands-on lab time.

The course is structured across three phases: Build (foundations), Engineer (architecture and agents), and Scale (production and growth).

Eight core topics spanning AI essentials, the AI life cycle, AI leadership, AI governance, AI tech stack and architecture, AI use cases, agentic engineering, and scaling AI systems into production.