Core Concepts
Foundations of probabilistic computing, stochastic evaluation, failure modes, and lifecycle governance.
Overview & Scope
This curriculum track covers the formal engineering specifications, verification methods, and runtime operational patterns required to master core concepts in enterprise production environments.
Curriculum Topics
Paradigm & Assumptions
The foundational shift in reasoning required for non-deterministic behavior and its four Core Assumptions.
Old Process Breakdown
Why traditional deterministic SDLC practices fail when applied to probabilistic models, and what replaces them.
The AI-Native Gap
Understanding the crucial split between Building WITH AI (developer workflow) and Building FOR AI (product capabilities).
The NUP Lifecycle for Probabilistic Software
A 6-stage engineering and governance lifecycle designed specifically for AI-native and non-deterministic software systems.
Evidence Requirements for Probabilistic Systems
A checklist-style reference of required continuous and point-in-time evidence artifacts for regulated AI software.
Regulatory Framework Coverage
A scannable two-column reference contrasting traditional software frameworks adapted for AI against emerging AI-specific regulations and standards.
Documentation & Artifacts
A single inventory of templates, document types, and RACI ownership for AI development governance, product safety, and technical communications.
Integration with an existing QMS
Extending rather than replacing certified enterprise Quality Management Systems (ISO 9001, ISO 13485, GAMP 5) for AI-native software.
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Community Discussion & Feedback
Attributed peer feedback and official Netspective architecture notes.