Model Registry (Kanban) - Entrenar - Training & Optimization Library
Description: A comprehensive guide to building neural network training systems with autograd, optimizers, LoRA/QLoRA, and quantization using EXTREME TDD methodology

# Model Registry

Manage model lifecycle with staging workflows, promotion policies, and version comparisons.

## Toyota Principle: Kanban

Visual workflow management for model stages. Models flow through stages (Development → Staging → Production → Archived) like parts through a factory.

## Quick Start

    #![allow(unused)]
    fn main() {
    use entrenar::storage::{InMemoryRegistry, ModelRegistry, ModelStage};
    use std::collections::HashMap;

    let mut registry = InMemoryRegistry::new();

    // Register a new model version
    let metrics = HashMap::from([
        ("accuracy".to_string(), 0.95),
        ("f1_score".to_string(), 0.93),
    ]);

    registry.register_model(
        "sentiment-classifier",
        1,
        metrics,
        "/artifacts/model-v1.safetensors"
    )?;

    // Transition through stages
    registry.transition_stage("sentiment-classifier", 1, ModelStage::Staging)?;
    registry.transition_stage("sentiment-classifier", 1, ModelStage::Production)?;
    }

## Model Stages

    #![allow(unused)]
    fn main() {
    use entrenar::storage::ModelStage;

    // Available stages
    ModelStage::Development  // Initial development
    ModelStage::Staging      // Testing and validation
    ModelStage::Production   // Serving traffic
    ModelStage::Archived     // Retired from use
    }

## Stage Transitions

    #![allow(unused)]
    fn main() {
    // Valid transitions
    // Development → Staging
    registry.transition_stage("model", 1, ModelStage::Staging)?;

    // Staging → Production
    registry.transition_stage("model", 1, ModelStage::Production)?;

    // Any stage → Archived
    registry.transition_stage("model", 1, ModelStage::Archived)?;

    // Invalid transitions return error
    // Cannot go backwards: Production → Staging (error)
    }

## Version Comparison

    #![allow(unused)]
    fn main() {
    use entrenar::storage::VersionComparison;

    // Compare two versions
    let comparison = registry.compare_versions("model", 1, 2)?;

    println!("Version {} vs {}", comparison.version_a, comparison.version_b);

    for (metric, diff) in &comparison.metric_diffs {
        println!("  {}: {:.2}% change", metric, diff.percent_change);
    }
    }

## Promotion Policies

Automatically validate models before promotion:

    #![allow(unused)]
    fn main() {
    use entrenar::storage::{PromotionPolicy, MetricRequirement, Comparison};

    let policy = PromotionPolicy::new()
        .require_metric(MetricRequirement {
            name: "accuracy".to_string(),
            comparison: Comparison::Gte,
            threshold: 0.90,
        })
        .require_metric(MetricRequirement {
            name: "latency_p99_ms".to_string(),
            comparison: Comparison::Lte,
            threshold: 100.0,
        });

    // Check if model meets policy
    let result = registry.check_promotion_policy("model", 1, &policy)?;

    if result.passed {
        registry.transition_stage("model", 1, ModelStage::Production)?;
    } else {
        for failure in result.failures {
            eprintln!("Policy violation: {}", failure);
        }
    }
    }

## Querying the Registry

    #![allow(unused)]
    fn main() {
    // Get specific version
    let version = registry.get_version("model", 1)?;

    // List all versions
    let versions = registry.list_versions("model")?;

    // Get models by stage
    let production_models = registry.get_by_stage(ModelStage::Production)?;

    // Get latest version in stage
    let latest = registry.get_latest_in_stage("model", ModelStage::Production)?;
    }

## Model Version Details

    #![allow(unused)]
    fn main() {
    use entrenar::storage::ModelVersion;

    let version: ModelVersion = registry.get_version("model", 1)?;

    println!("Version: {}", version.version);
    println!("Stage: {:?}", version.stage);
    println!("Created: {}", version.created_at);
    println!("Artifact: {}", version.artifact_path);

    for (name, value) in &version.metrics {
        println!("  {}: {}", name, value);
    }
    }

## Audit Trail

Track all stage transitions:

    #![allow(unused)]
    fn main() {
    use entrenar::storage::StageTransition;

    let transitions = registry.get_transitions("model", 1)?;

    for t in transitions {
        println!(
            "{}: {:?} → {:?}",
            t.timestamp, t.from_stage, t.to_stage
        );
    }
    }

## Cargo Run Example

    # Register and promote a model
    cargo run --example model_registry

    # List all production models
    cargo run --example model_registry -- --list-production

## Integration with CI/CD

    # .github/workflows/model-promotion.yml
    name: Model Promotion

    on:
      workflow_dispatch:
        inputs:
          model_name:
            required: true
          version:
            required: true

    jobs:
      promote:
        runs-on: ubuntu-latest
        steps:
          - uses: actions/checkout@v4
          - name: Check promotion policy
            run: |
              cargo run --example check_promotion -- \
                --model ${{ inputs.model_name }} \
                --version ${{ inputs.version }}
          - name: Promote to production
            if: success()
            run: |
              cargo run --example promote_model -- \
                --model ${{ inputs.model_name }} \
                --version ${{ inputs.version }} \
                --stage production

## Best Practices

1. Define clear promotion policies - Objective criteria for each stage
2. Keep audit trails - Track who promoted what and when
3. Use semantic versioning - Clear version numbering
4. Archive old versions - Don't delete, archive for rollback
5. Automate promotion checks - CI/CD integration

## See Also

- MLOps Overview
- Experiment Tracking
- Cloud Storage