Udemy - Complete AI Architecture Bootcamp - From RAG to Agents

Complete AI Architecture Bootcamp: From RAG to Agents

https://WebToolTip.com

Published 6/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 8h 37m | Size: 2.59 GB

Build Enterprise AI Solutions with LLMs, Agents, MCP, Automation, Data Platforms, and Security

What you'll learn
Design complete Enterprise AI Architectures that align business requirements with scalable AI solutions.
Build and evaluate AI Agent and Multi-Agent Systems for automation, decision-making, and workflow orchestration.
Architect Retrieval-Augmented Generation (RAG) platforms using embeddings, vector databases, document ingestion pipelines, and knowledge retrieval systems.
Design and integrate LLM-powered applications using modern models such as ChatGPT, Claude, Gemini, and open-source alternatives.
Create MCP-enabled AI environments that connect AI systems with APIs, databases, SaaS applications, and enterprise tools.
Develop AI Automation Architectures that incorporate human-in-the-loop workflows, monitoring, exception handling, and process optimization.
Design AI-ready Data Architectures including data pipelines, warehouses, lakes, knowledge repositories, and real-time data systems.
Apply AI Security, Governance, and Responsible AI Frameworks to ensure compliance, risk management, auditability, and trustworthy AI deployment.
Architect scalable Cloud and Infrastructure Solutions for deploying AI applications across SaaS, enterprise, hybrid, and edge environments.
Produce professional AI Architecture Documentation, Solution Roadmaps, and Executive Presentations for stakeholders and clients.
Evaluate architectural trade-offs and make informed build-versus-buy decisions for enterprise AI initiatives.

Requirements
No prior AI experience is required. This course is designed to take you from foundational concepts to advanced AI architecture design.
Basic computer literacy and familiarity with common business software and web applications are recommended.
A general understanding of technology concepts such as applications, databases, APIs, or cloud services is helpful but not mandatory.
No programming experience is required, although learners with technical backgrounds may find some topics easier to grasp.
A computer with internet access is required to follow nstrations, complete labs, and explore AI tools and platforms.