AI Orchestration: Developing and Testing Your AI Prototype
58mIntermediate2025-04-09
Authors

Nayan Saxena
Course details
This course takes AI developers through the process of building and testing an AI prototype, with a focus on hands-on implementation. Instructor Nayan Saxena begins by showing you how to set up a basic prototype and integrate your AI models. He covers best practices in testing and debugging AI models, along with tools to help streamline the orchestration and testing process.
Learning objectives
Build an MVP (minimum viable product) AI prototype.
Test and debug an AI prototype using relevant tools.
Understand the process of integrating AI models into a prototype environment.
Apply best practices in testing, debugging, and iterating on AI models.
Use orchestration tools to manage and collaborate on AI prototypes.
Learning objectives
Build an MVP (minimum viable product) AI prototype.
Test and debug an AI prototype using relevant tools.
Understand the process of integrating AI models into a prototype environment.
Apply best practices in testing, debugging, and iterating on AI models.
Use orchestration tools to manage and collaborate on AI prototypes.
Skills covered
Software Quality AssuranceArtificial Intelligence FoundationsArtificial Intelligence (AI)Software DevelopmentOne-Off
Concepts
0. Introduction
- 01 - Developing an AI prototype
- 02 - What you should know
1. Building an AI Prototype
- 03 - What is an AI MVP
- 04 - Choosing tools and frameworks for AI prototyping
- 05 - Building an MVP from scratch
2. Debugging and Troubleshooting AI Applications
- 06 - Common AI prototype issues
- 07 - Debugging overfitting and underfitting
- 08 - Handling data-related errors
3. Testing and Debugging Your AI Prototype
- 09 - Why testing matters in AI development
- 10 - Unit testing for AI components
- 11 - Performance testing your AI model
4. Preparing Your Prototype for Production
- 12 - Transitioning from prototype to production
- 13 - Optimizing your prototype for scalability
- 14 - Common production pitfalls
Conclusion
- 15 - Next steps and additional resources