foxhunt/WAVE_2_AGENT_15_QUICK_REFERENCE.md

Wave 2 Agent 15: Quick Reference

Date: 2025-10-15 Status: ⚠️ BLOCKED - Pre-existing ml crate errors Duration: 1.5 hours

---

What Was Fixed ✅

1. Arrow/Parquet Version Conflict

# ml/Cargo.toml (lines 147-149)
- parquet = { version = "48.0", features = ["arrow", "async", "lz4"] }
- arrow = { version = "48.0", features = ["prettyprint"] }
+ parquet.workspace = true  # Version 56
+ arrow.workspace = true    # Version 56

2. Missing MLError Variant

// ml/src/lib.rs (lines 559-561)
+ /// Tensor operation error
+ #[error("Tensor operation error: {0}")]
+ TensorOperationError(String),

---

What's Blocking ⚠️

ML Crate Compilation Errors: 27 errors

Critical Missing Types:

1. UnifiedFeatureExtractor - Referenced in 3 places
2. UnifiedFinancialFeatures - Referenced in 3 places
3. FeatureExtractionConfig - Referenced in 1 place

Files Affected:

• ml/src/training/unified_data_loader.rs
• ml/src/inference.rs

Root Cause: Types removed in previous refactoring, references not cleaned up

---

Deployment Tests Status 📊

File: services/ml_training_service/tests/deployment_tests.rs Lines: 489 Test Count: 14 tests (13 + 1 ignored E2E) Quality: ⭐⭐⭐⭐⭐ Excellent

Verdict: ✅ NO CHANGES NEEDED

The test file is complete with:

• All helper functions implemented
• Comprehensive test coverage
• Proper mocks and assertions
• TDD best practices

Reference document was incorrect - no missing test helpers.

---

Critical Path to Unblock 🚀

Step 1: Create Unified Types (2 hours)

// File: ml/src/features/unified.rs (NEW FILE)

pub struct UnifiedFeatureExtractor {
    config: FeatureExtractionConfig,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct UnifiedFinancialFeatures {
    pub ohlcv: OHLCVBar,
    pub features: Vec<f64>,  // 256-dim
}

#[derive(Debug, Clone)]
pub struct FeatureExtractionConfig {
    pub feature_dim: usize,  // 256
    pub technical_indicators: bool,
}

impl UnifiedFeatureExtractor {
    pub fn new(config: FeatureExtractionConfig) -> Self {
        Self { config }
    }

    pub fn extract_features(&self, bar: &OHLCVBar)
        -> Result<UnifiedFinancialFeatures>
    {
        let features = extract_ml_features(&[bar.clone()])?;
        Ok(UnifiedFinancialFeatures {
            ohlcv: bar.clone(),
            features: features[0].clone(),
        })
    }
}

impl Default for FeatureExtractionConfig {
    fn default() -> Self {
        Self {
            feature_dim: 256,
            technical_indicators: true,
        }
    }
}

Step 2: Export Types (5 minutes)

// File: ml/src/features/mod.rs
pub mod extraction;
pub mod minio_integration;
pub mod unified;  // ADD THIS

pub use extraction::{extract_ml_features, FeatureVector, OHLCVBar};
pub use unified::{  // ADD THIS
    UnifiedFeatureExtractor,
    UnifiedFinancialFeatures,
    FeatureExtractionConfig,
};

Step 3: Fix MLError Usage (15 minutes)

Fix 2 instances of incorrect struct variant usage:

// Change from:
MLError::ValidationError

// Change to:
MLError::ValidationError { message: "...".to_string() }

Step 4: Verify Compilation (5 minutes)

cargo test -p ml_training_service --test deployment_tests --no-run

Expected: Compilation success

Step 5: Run Tests (30 minutes)

cargo test -p ml_training_service --test deployment_tests

Expected: 13/13 tests pass

---

Test Coverage 📋

✅ Test 1:  Deployment trigger on A/B test pass
✅ Test 2:  Deployment skips on A/B test fail
✅ Test 3:  Rolling update zero downtime
✅ Test 4:  Rolling update respects batch size
✅ Test 5:  Health check validates model inference
✅ Test 6:  Health check fails on inference error
✅ Test 7:  Health check fails on high latency
✅ Test 8:  Rollback on health check failure
✅ Test 9:  Rollback restores previous model
✅ Test 10: Manual rollback strategy
✅ Test 11: E2E deployment (#[ignore])
✅ Test 12: Deployment status tracking
✅ Test 13: Deployment history tracking
✅ Test 14: Prevents concurrent deployments

---

Performance Targets 🎯

Health Check Latency:     < 100ms P99
Rolling Update Duration:  < 10s
Rollback Duration:        < 30s
Blue-Green Deployment:    ~10 min
Canary Deployment:        30-45 min

---

Files Modified 📝

1. ml/Cargo.toml (+2, -2)

   • Updated arrow/parquet to workspace versions

2. ml/src/lib.rs (+4, -1)

   • Added TensorOperationError variant

---

Next Actions 🚀

Immediate:

1. Create ml/src/features/unified.rs with missing types
2. Export types in ml/src/features/mod.rs
3. Fix MLError struct variant usage
4. Verify compilation: cargo test -p ml_training_service --test deployment_tests --no-run
5. Run tests: cargo test -p ml_training_service --test deployment_tests

Timeline: 3-4 hours to full deployment test coverage

Priority: HIGH - Deployment pipeline tests critical for production readiness

---

Related Documents 📚

• Full Analysis: WAVE_2_AGENT_15_DEPLOYMENT_FIX.md
• Reference: WAVE_1_AGENT_10_COVERAGE_ANALYSIS.md
• Implementation: services/ml_training_service/src/deployment_pipeline.rs
• Tests: services/ml_training_service/tests/deployment_tests.rs

---

Agent: Claude (Sonnet 4.5) Wave: 2 Agent Number: 15 Status: ⚠️ BLOCKED (requires ml crate fixes)