Testing AI, AI Driven Systems, and Machine Learning Pipelines | Webinar | Curiosity Software
AI Summary
Overview of Testing AI-Driven Systems
- Introduction
- Speaker: Ben Johnson Ward, BB Solutions Engineering.
- Focus on testing AI-driven systems including machine learning (ML) pipelines.
- Purpose of Discussion
- Overview of challenges in testing AI applications.
- Need for quality assurance as AI adoption grows (35% of businesses utilize AI).
- New Challenges
- AI systems are increasingly complex; traditional testing methods may not suffice.
- Shift in testing focus required due to:
- Non-deterministic outputs.
- Massive input spaces.
- Unique failure modes.
- Key Concepts
- Different types of AI-Driven Applications:
- Standard AI applications.
- Learning pipelines (continuous learning and model updates).
- Testing actual AI models (less frequent).
- Testing AI-Driven Applications
- Scope must include service interfaces and how they handle AI outputs.
- Importance of mocking to test service functionality without the AI component.
- Testing inputs: managing context within input limits and ensuring data validity.
- Testing outputs: ensuring the service handles responses correctly and gracefully.
- Challenges in Learning Pipelines
- Quality assurance must include data cleaning and pipeline testing.
- Use synthetic, fictitious data for effective testing of training processes.
- Direct AI Testing
- Rare to test AI models directly; focus is more on integration validation.
- Functional testing can include:
- Property-based testing (checking for known output characteristics).
- Adversarial testing to expose vulnerabilities.
- Conclusion
- Essential to adapt testing strategies to match evolving AI technologies.
- Emphasize monitoring the effectiveness and accuracy of AI applications over time.