Generative AI, data pipelines, and model lifecycle concepts across major cloud platforms
Artificial Intelligence is rapidly moving into the cloud , transforming how organizations build, scale, and deliver intelligent applications. This course provides a comprehensive foundation for understanding how AI services operate across the world’s leading cloud platforms: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).
What you’ll learn
- how Generative AI and Large Language Models integrate within cloud ecosystems..
- Differentiate between major cloud AI platforms.
- Understand the role of data pipelines, object storage, and warehousing in AI infrastructure..
- escribe key concepts in model lifecycle management : training, evaluation, and export..
Course Content
- Cloud-Native Foundations for AI –> 6 lectures • 1hr 2min.
- AI & Foundation Model Fundamentals –> 6 lectures • 32min.
- Multi-Cloud AI Fast Start (AWS | Azure | GCP) –> 4 lectures • 16min.
- Data & ML Foundations on Cloud –> 3 lectures • 5min.
- TensorFlow & Model Serving Foundations –> 2 lectures • 4min.
Requirements
Artificial Intelligence is rapidly moving into the cloud , transforming how organizations build, scale, and deliver intelligent applications. This course provides a comprehensive foundation for understanding how AI services operate across the world’s leading cloud platforms: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).
Through clear explanations and structured platform overviews, you will explore how cloud providers host, train, and manage large-scale AI models. The learning journey begins with the fundamentals of Generative AI, Large Language Models (LLMs), and Foundation Models, showing how they are integrated into managed cloud ecosystems such as Vertex AI, Azure AI Studio, and Amazon Bedrock.
You will then discover the essential data infrastructure that powers cloud-based AI systems, including object storage patterns, ETL/ELT pipelines, and data warehousing concepts. The course concludes with a focus on model lifecycle management, covering training, evaluation, and model export workflows that support scalable deployment.
By the end of this course, you will gain a clear conceptual understanding of how modern cloud environments deliver and operationalize AI at scale. Whether you aim to advance your career in AI strategy, data architecture, or cloud engineering, this program equips you with the knowledge to navigate the evolving landscape of multi-cloud AI systems confidently.