Foundations of Cloud AI: Understanding Modern AI Platforms

Name: Foundations of Cloud AI: Understanding Modern AI Platforms
Rating: 4.5 (1679 reviews)

Generative AI, data pipelines, and model lifecycle concepts across major cloud platforms

Highest Rated

Created byIng.Seif | Europe Innovation

Last updated 10/2025

English

English [Auto],Korean [Auto],

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
Discover how containers and Docker images, built in layers and stored in registries, package the entire runtime to run AI workloads consistently across laptops, clouds, and on-prem clusters.
From Docker Compose to Kubernetes11:04
Docker Compose and Kubernetes move from local orchestration to cloud-scale deployment, enabling resilient, scalable AI services through pods, deployments, and services.
Kubernetes Core Objects (Pods, Deployments, Services)2:02
Learn how Kubernetes uses pods, deployments, and services to run, scale, and expose apps with a stable endpoint, automatic self-healing, and rolling updates.
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
Automate code from commit to production with CI/CD pipelines for cloud-native apps, building reproducible Docker images, deploying to Kubernetes, and integrating security, observability, and blue-green canary rolling deployments.

Introduction to Generative AI Systems5:59
Discover how generative AI creates text, images, music, and video. Learn how specialized models and chatbots drive business productivity and innovation.
Inside Large Language Models (LLMs)6:48
Discover how generative AI creates text, images, audio, and synthetic data, and how large language models rely on pre-training and fine-tuning on large datasets for chat, presentations, and education tasks.
The Strengths and Limitations of LLMs5:23
Explore the strengths and limitations of large language models, from ready-to-use capabilities like translation and code generation to fine tuning, foundation models, hallucinations, bias, data privacy, and cost considerations.
Families of Modern Language Models6:24
Compare families of modern language models from GPT generations to palm two and Anthropic LLM cloud, highlighting multimodal inputs, 100k context windows, and open-source options.
How Foundation Models Are Built and Trained2:51
Discover how foundation models are pre-trained on vast data, then fine-tuned for specific tasks across text, images, and audio, enabling rapid, adaptable AI deployments.
How Foundation models Work4:19
Explore how foundation models operate by examining transformer architecture, activation gates, and the attention mechanism, from basic through advanced layers, with tuning variables and error evaluators refining performance.

Getting Started with Vertex AI (GCP)6:22
Vertex AI unifies the ML lifecycle—from datasets to deployment—with pipelines, model registry, feature store, and no-code Vertex AI studio for Gemini models.
Exploring Azure AI Foundry and Studio5:34
Discover Azure AI Foundry, a workspace that merges Azure Machine Learning, cognitive services, and Azure Open AI into a studio to build, connect, and deploy AI solutions with pre-built modules.
Introduction to Amazon Bedrock & SageMaker2:33
Explore Amazon Bedrock's managed foundation models via a single API for rapid text, image, or embeddings, and learn SageMaker's full ML platform for training and deployment.
Cost, Security, and Governance for AI Systems1:42
Explore how SageMaker, Vertex AI, and Azure Machine Learning manage costs, enforce responsible AI governance, and secure data with autoscaling, spot instances, VPC isolation, and versioned registries.

Object Storage Patterns (S3, Blob, GCS)1:49
Object storage forms the backbone of AI data across AWS S3, Azure Blob, and Google Cloud Storage, using buckets, objects, and simple APIs with versioning, encryption, and IAM access.
ETL/ELT Pipelines (Glue, Data Factory, Dataflow)1:41
Understand how ETL and ELT pipelines prepare data from multiple sources for AI models, using cloud tools like AWS Glue, Azure Data Factory, and Google Dataflow for scalable transformations.
Warehousing Fundamentals (BigQuery, Redshift, Synapse)1:07
Store and query structured data at petabyte scale to fuel analytics and AI modeling, with partitioning and caching to reduce costs, while BigQuery, Redshift, and Synapse feed feature stores.

Keras Model Building (Sequential & Functional)1:50
Keras, API of TensorFlow, simplifies building, training, and exporting models with sequential and functional architectures. Compile using an optimizer, a loss, and metrics, then verify with Model.summary and plot model.
Training, Evaluation, and Checkpointing1:44
Train through epochs with forward passes, loss, backpropagation, and weight updates to learn patterns; use training, validation, and test sets with checkpoints to save the best model.

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.

Foundations of Cloud AI: Understanding Modern AI Platforms

What you'll learn

Explore related topics

Course content

Cloud-Native Foundations for AI6 lectures • 1hr 2min

AI & Foundation Model Fundamentals6 lectures • 32min

Multi-Cloud AI Fast Start (AWS | Azure | GCP)4 lectures • 16min

Data & ML Foundations on Cloud3 lectures • 5min

TensorFlow & Model Serving Foundations2 lectures • 4min

Requirements

Description

Who this course is for: