Overview

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This course explores how Generative AI is transforming modern cloud solutions by combining large language models with scalable cloud architectures. Learners gain a strategic understanding of how AI-driven systems are designed, deployed, and governed in real-world cloud environments. Through a structured journey from NLP foundations to advanced LLM-based application development, the course helps learners build practical skills in fine-tuning models, retrieval-augmented generation, and prompt engineering. You will learn how to design, deploy, and scale AI-powered cloud applications while addressing operational and performance challenges. What sets this course apart is its balance of conceptual depth and hands-on architectural thinking. It connects core AI concepts with real-world cloud deployment patterns, Dev frameworks, and LLMOps practices. This course is ideal for cloud engineers, software developers, architects, and technology professionals looking to integrate Generative AI into cloud solutions. A basic understanding of cloud computing and software development concepts is recommended.

Syllabus

Cloud Computing Meets Generative AI Bridging Infinite Impossibilities

In this section, we explore conversational AI and generative AI, focusing on LLMs, open source vs closed source models, and cloud computing for scalable AI implementation.

NLP Evolution and Transformers Exploring NLPs and LLMs

In this section, we explore NLP evolution and the role of transformers in AI communication and model development.

Fine-Tuning Building Domain-Specific LLM Applications

In this section, we cover domain-specific LLM fine-tuning, PEFT, and evaluation methods to improve accuracy and reliability.

RAGs to Riches Elevating AI with External Data

In this section, we explore retrieval-augmented generation (RAG) to enhance LLM accuracy, focusing on vector databases, chunking strategies, and real-world applications like chatbots and recommendation systems.

Effective Prompt Engineering Techniques Unlocking Wisdom Through AI

In this section, we explore prompt engineering techniques, emphasizing RAG integration, LLM interaction design, and ethical considerations for effective AI applications.

Developing and Operationalizing LLM-based Apps Exploring Dev Frameworks and LLMOps

In this section, we explore generative AI app development frameworks like Semantic Kernel and LangChain, autonomous agents, and LLMOps for operationalizing LLM-based applications.

Deploying ChatGPT in the Cloud Architecture Design and Scaling Strategies

In this section, we explore scaling ChatGPT in cloud environments, analyzing TPM, RPM, and PTU limits, and designing enterprise-ready architectures for efficient and reliable generative AI solutions.

Security and Privacy Considerations for Gen AI Building Safe and Secure LLMs

In this section, we examine security and privacy challenges in generative AI, focusing on risk mitigation, access controls, and secure deployment strategies for LLMs.

Responsible Development of AI Solutions Building with Integrity and Care

In this section, we explore responsible AI (RAI) principles, address LLM challenges, and evaluate Deepfake risks to ensure ethical, transparent, and safe AI development.