Cloud-Based AI Solution Design Patterns

53mIntermediate2025-04-14

Authors

Thomas Erl

Course details

How do you build scalable AI data architectures in the cloud? What are the best techniques for optimizing distributed AI model training in cloud environments? How can you design resilient and cost-effective AI applications that utilize cloud-based autoscaling and serverless inference? This course delves into proven design patterns tailored for cloud-based AI, addressing challenges in data architecture, model training optimization, and resilient and scalable application deployment and operations. LinkedIn Top Voice and author, Thomas Erl, explains these techniques, and how to use them to create highly effective AI solutions designed to leverage cloud-based services, resources and infrastructure.

Skills covered

Software Design PatternsAI for Business FoundationsArtificial Intelligence for BusinessCloud ServicesCloud ComputingSoftware DevelopmentOne-Off

Concepts

0. Introduction

01 - Introduction
02 - What you need to know
03 - Systems and platforms in the cloud
04 - Understanding design patterns

1. Cloud-Based Data-Centric Design Patterns

05 - Data-centric design patterns overview
06 - Serverless data pipeline
07 - Distributed feature store
08 - Continuous data validation
09 - Hybrid data processing

2. Cloud-Based Model-Centric Design Patterns

10 - Model-centric design patterns overview
11 - Distributed AI-model training
12 - AI-model operations monitoring and optimization
13 - AI-model drift detection
14 - Federated AI learning

3. Cloud-Based Application-Centric Design Patterns

15 - Application-centric design patterns overview
16 - AI-workload autoscaling
17 - Containerized AI-model deployment
18 - Serverless AI inference

Conclusion

19 - Next steps