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Foundations of Cloud AI: Understanding Modern AI Platforms
Rating: 4.4 out of 5(808 ratings)
2,171 students

Foundations of Cloud AI: Understanding Modern AI Platforms

Generative AI, data pipelines, and model lifecycle concepts across major cloud platforms
Created byIng.Seif | Europe Innovation
Last updated 10/2025
English

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.
  • Describe key concepts in model lifecycle management : training, evaluation, and export.

Course content

5 sections • 21 lectures • 1h 58m total length

  • 12-Factor Principles24:43

    Discover the 12 factor principles for building cloud-native, portable apps from code base. Learn how dependencies, configuration via environment variables, and backing services enable scalable deployments across development to production.

  • Containers & Image Packaging (Docker Essentials)10:24
  • From Docker Compose to Kubernetes11:04
  • Kubernetes Core Objects (Pods, Deployments, Services)2:02
  • Infrastructure as Code with Terraform (Foundations)3:33

    Discover how infrastructure as code with Terraform replaces manual cloud setup with declarative, portable configurations in HTL, enabling consistent environments across clouds and CI/CD integration.

  • CI/CD for Cloud-Native Apps10:41

Requirements

  • No technical or programming background required. A general understanding of cloud computing is helpful but not mandatory.

Description

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.

Who this course is for:

  • Students and beginners who want to understand how cloud platforms enable modern AI solutions.
  • Business professionals and managers seeking a high-level overview of AI services across AWS, Azure, and Google Cloud.
  • IT and data professionals transitioning into AI or cloud roles who need conceptual clarity before diving into tools.

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