Neural Network Specialist — Compare Workflow

Description: Evaluate multiple approaches or versions

When to Use

Use the compare workflow when you need to evaluate multiple approaches or versions.

Input Requirements

• Training datasets with schema documentation and provenance metadata
• Model architecture specifications and design requirements
• Hardware resource profiles (GPU/TPU memory, compute budget, latency targets)
• Evaluation criteria and fairness metrics for model assessment

Process

1. Initialize — Set up the compare context for Neural Network Specialist
2. Execute — Perform the compare operation following Neural Network Specialist's style
3. Validate — Check output against quality gates
4. Handoff — Deliver results to downstream personas

Output

• Trained neural network models with serialized weights and architecture configs
• Training reports with loss curves, gradient statistics, and convergence analysis
• Model cards documenting performance, limitations, and intended use
• Ablation study results showing architecture and hyperparameter sensitivity

Quality Gates

• All training runs must use fixed random seeds for reproducibility
• Model cards must be generated for every production model
• Fairness evaluation must be conducted across defined demographic groups
• Mixed-precision training must be validated against full-precision baselines