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.
Generative AI for Cloud Solutions
Generative AI for Cloud Solutions
Instructor: Packt - Course Instructors
Access provided by L&T Corp - ATLNext
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Understand the fundamentals of generative AI, large language models, and their integration with cloud platforms.
Explore advanced techniques such as fine-tuning, prompt engineering, and responsible AI practices.
Gain insights into the development and deployment of AI applications using frameworks like LLMOps and Assistants APIs.
Skills you'll gain
- Prompt Engineering
- Multimodal Prompts
- Retrieval-Augmented Generation
- Model Deployment
- Vector Databases
- Cloud Deployment
- Responsible AI
- ChatGPT
- Cloud Computing
- Natural Language Processing
- Model Evaluation
- AI Security
- Large Language Modeling
- LangChain
- Cloud Computing Architecture
- Generative AI
- Scalability
- Skills section collapsed. Showing 10 of 17 skills.
Details to know
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Assessments
10 assignments
Taught in English
Recently updated!
February 2026
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There are 10 modules in this course
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.
What's included
2 videos9 readings1 assignment
In this section, we explore NLP evolution and the role of transformers in AI communication and model development.
What's included
1 video5 readings1 assignment
In this section, we cover domain-specific LLM fine-tuning, PEFT, and evaluation methods to improve accuracy and reliability.
What's included
1 video6 readings1 assignment
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.
What's included
1 video7 readings1 assignment
In this section, we explore prompt engineering techniques, emphasizing RAG integration, LLM interaction design, and ethical considerations for effective AI applications.
What's included
1 video4 readings1 assignment
In this section, we explore generative AI app development frameworks like Semantic Kernel and LangChain, autonomous agents, and LLMOps for operationalizing LLM-based applications.
What's included
1 video8 readings1 assignment
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.
What's included
1 video5 readings1 assignment
In this section, we examine security and privacy challenges in generative AI, focusing on risk mitigation, access controls, and secure deployment strategies for LLMs.
What's included
1 video8 readings1 assignment
In this section, we explore responsible AI (RAI) principles, address LLM challenges, and evaluate Deepfake risks to ensure ethical, transparent, and safe AI development.
What's included
1 video8 readings1 assignment
In this section, we explore future AI trends, including multimodal interactions and ChatGPT's evolving trajectory.
What's included
1 video5 readings1 assignment
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