Overview

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Explore the essential principles and frameworks for building scalable, trustworthy, and modern AI-ready enterprise architectures. Learn how to bridge modernization with innovation and establish robust governance for successful AI adoption. This course introduces the foundational concepts required to transform traditional enterprise systems into AI-ready architectures. Learners will examine the evolution of GenAI, RAG, and agentic AI, understand the importance of modernization, and discover how to design unified data access and governance frameworks. The course also covers strategies for ensuring trustworthy AI, compliance, and effective data governance, providing a strong base for enterprise leaders and architects to drive AI initiatives. Through a blend of real-world examples, reference patterns, and clear explanations, this course guides learners from strategic concepts to practical frameworks for AI modernization and governance. Learners will gain actionable insights to align enterprise systems with the demands of intelligent, adaptive AI solutions. This course is part one of a three-course Specialization designed to build a complete and cohesive understanding of the subject. While it offers valuable skills on its own, you'll gain the most benefit by progressing through all three courses as a structured learning journey. This course is based on Architectures for the Intelligent AI-Ready Enterprise, by Boris Bialek, Sebastian Rojas Arbulu and Taylor Hedgecock. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.

Syllabus

AI Modernization to Innovation
- This module provides an in-depth look at the strategic differences between AI modernization and innovation, helping learners navigate the challenges of AI implementation and avoid common pitfalls. It explores how organizations can build a strong foundation for AI adoption and align innovation with sustainable practices. Learners will gain insights into planning and executing AI strategies effectively.
What Sets GenAI, RAG, and Agentic AI Apart
- This module provides an in-depth look at the differences between Generative AI, RAG, and Agentic AI, focusing on how each system processes data, generates content, and applies knowledge. Learners will gain an understanding of foundational AI technologies and their practical implementations in real-world scenarios.
The System of Action
- This module explores the key components of system design for real-time AI, including data access, integration, and governance. Learners will gain insights into how enterprises can build robust GenAI solutions that support business outcomes while addressing security and compliance challenges. The content covers practical strategies for handling multimodal data and ensuring data quality.
Trustworthy AI, Compliance, and Data Governance
- This module explores the key principles of trustworthy AI, regulatory compliance, and data governance. Learners will gain insights into how ethical frameworks, stakeholder engagement, and data practices contribute to building responsible AI systems. The module also covers real-world applications and challenges in implementing these concepts across industries.
Modernization Using AI
- This module explores strategies for modernizing legacy systems using AI, focusing on overcoming technical limitations, automating workflows, and leveraging AI for code transformation and deployment. Learners will gain insights into how AI can streamline processes, reduce risks, and drive innovation in system upgrades.