AI Solution Design Patterns: Data, Model Training, and Application Architectures
1h 9mIntermediate2025-04-03
Authors
Thomas Erl
Course details
How can we create a data architecture specifically for AI solutions? How can we optimize and automate model training? How can we design scalable, flexible, and resilient AI application architectures? These and other design problems are addressed by this collection of AI solution design patterns explained by LinkedIn Top Voice and author,Thomas Erl. Explore how AI solution design patterns provide proven techniques and best practices for building highly effective AI solutions and overcoming common design challenges
Skills covered
Software Design PatternsSoftware ArchitectureAI for Business FoundationsArtificial Intelligence for BusinessSoftware DevelopmentOne-Off
Concepts
0. Introduction
- 01 - Introduction
- 02 - What you need to know
- 03 - Understanding design patterns
1. Data-Centric Design Patterns
- 04 - Data-centric design patterns overview
- 05 - Data lake architecture
- 06 - Data warehouse
- 07 - Big data system
- 08 - Feature store
- 09 - Data pipeline orchestration
- 10 - Bringing it all together - AI data architecture
2. Model-Centric Design Patterns
- 11 - Model-centric design patterns overview
- 12 - Continuous learning
- 13 - Reinforcement learning
- 14 - Transfer learning
- 15 - Heuristic learning
- 16 - Ensemble learning
- 17 - Model pipeline orchestration
- 18 - Bringing it all together - Automating model training
3. Application-Centric Design Patterns
- 19 - Application-centric design patterns overview
- 20 - Distributed AI processing
- 21 - AI microservice architecture
- 22 - AI service containerization
- 23 - Service orchestration
- 24 - Bringing it all together - AI application architecture
Conclusion
- 25 - Integrating AI platforms
- 26 - Next steps
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