The conceptual heart of NeuronScope. Each chapter explains AI Observability, AI Evaluation, AI Profiling, Explainability, Workflow Intelligence, Optimization, and Governance.
Deterministic identity for models, datasets, prompts, workflows, and environments.
The captured runtime — OS, Python, packages, CUDA, seeds, and hardware state.
How NeuronScope observes model structure and agent decisions without modifying code.
Streaming, chunked, and mapped dataset adapters with reproducible ordering.
Structured prompts as first-class, hashable objects with context tracking.
Adapters for PyTorch, JAX, TensorFlow, LLMs, Vector DBs, and custom runtimes.
A recorded run with pinned inputs, full telemetry, and a portable artifact.
Repeatable, comparable evaluations over quality, accuracy, latency, and hallucination metrics.
CPU, GPU, memory, token consumption, and per-step timing.
The exact bit-exact contracts NeuronScope guarantees across platforms.
Rendered summaries and interactive dashboards produced from telemetry artifacts.
Structural and semantic checks over inputs, outputs, and security policies.
Diffing two AI runs, models, or workflows to detect regressions.
The .nsz format, its stability guarantees, and forward/backward compatibility.
How adapters, probes, evaluation metrics, and plugins register themselves.
The fluent builder used to construct experiments, benchmarks, and workflows.
The plugin contract, discovery, isolation, and enterprise versioning.
Every public class, function, and constant in the AI Engineering Platform.
Subsystem design of the AI Engineering Platform.
Task-oriented recipes for AI engineering.
Step-by-step walkthroughs for all experience levels.