Technical 7 Modules Enrolling Now

Applied Agentic AI for Digital Transformation

A technical course for building grounded, tool-using, stateful, observable, and production-ready AI systems.

menu_book 7 Modules, 49 Lessons
schedule 7h
quiz 7 Module Quizzes
assignment Applied Exercises

Who This Course Is For

Engineers and technical builders implementing AI systems in real environments.

What You Will Learn

  • check_circle Build practical capability in LLM Foundations and API Engineering
  • check_circle Build practical capability in Open-Source Data Engineering for AI
  • check_circle Build practical capability in RAG and Knowledge Assistants
  • check_circle Build practical capability in Applied Agentic AI
  • check_circle Build practical capability in Multi-Agent Systems
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$299.00 Full Access

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This course includes:

  • 7 structured learning modules
  • 49 lessons
  • 7 module quizzes
  • Applied exercises and labs
  • Progress tracking dashboard

Course Curriculum

7 modules designed as a logical progression for applied outcomes.

description What Makes Data AI-Ready
description Reference Architectures for AI Data Systems
description PostgreSQL, pgvector, and Unified State Patterns
description Orchestration, Transformation, and Refresh Cycles
description Metadata, Governance, and Data Quality for AI Systems
description Module Summary
description Module LAB Exercise
description Why RAG Exists and When It Helps
description Ingestion, Chunking, and Document Preparation
description Retrieval Strategies: Keyword, Vector, Hybrid, and Reranking
description Knowledge Assistant Design: Answer Construction, Grounding, and Trust
description Evaluation, Failure Analysis, and Advanced Retrieval Directions
description Module Summary
description Module LAB Exercise
description What Makes a System Agentic
description Tool Use, Action Loops, and ReAct Patterns
description State, Memory, and Episodic Continuity
description Context Engineering and Workflow Control
description Human-in-the-Loop, Interruptions, and Safe Action Design
description Module Summary
description Module LAB Exercise
description Why and When to Use Multi-Agent Systems
description Orchestration Topologies and Control Patterns
description Role-Based Agent Teams and Specialist Design
description Shared State, Handoffs, and Collaboration Protocols
description Multi-Agent Failure Modes, Graph Reasoning, and Advanced Extensions
description Module Summary
description Module LAB Exercise
description From Prototype to Production
description Observability: Logs, Traces, and Runtime Visibility
description Versioning Prompts, Models, Tools, and Configurations
description Latency, Throughput, and Cost Engineering
description Controlled Releases, Feedback Loops, and Governed Improvement
description Module Summary
description Module LAB Exercise
description Why AI Systems Require Different Testing Approaches
description Designing Evals for LLM, RAG, and Agent Systems
description Red Teaming, Prompt Injection, and GenAI Security Risks
description Guardrails, Validation Layers, and Policy Enforcement
description Human Review, Governance, and Final Trust Decisions
description Module Summary
description Module LAB Exercise
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