diff --git a/ml/src/checkpoint/compression.rs b/ml/src/checkpoint/compression.rs
index e2283f33a..9c255485d 100644
--- a/ml/src/checkpoint/compression.rs
+++ b/ml/src/checkpoint/compression.rs
@@ -282,7 +282,7 @@ impl CompressionStats {
 #[cfg(test)]
 mod tests {
     use super::*;
-    // use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_compression_manager() -> Result<(), MLError> {
diff --git a/ml/src/checkpoint/mod.rs b/ml/src/checkpoint/mod.rs
index 660994465..921be828d 100644
--- a/ml/src/checkpoint/mod.rs
+++ b/ml/src/checkpoint/mod.rs
@@ -872,7 +872,7 @@ impl CheckpointManager {
 mod tests {
     use super::*;
     use std::sync::atomic::AtomicBool;
-    // use crate::safe_operations; // DISABLED - module not found
+
 
     // Mock model for testing
     struct MockModel {
diff --git a/ml/src/checkpoint/validation.rs b/ml/src/checkpoint/validation.rs
index 53c243bf9..89387439e 100644
--- a/ml/src/checkpoint/validation.rs
+++ b/ml/src/checkpoint/validation.rs
@@ -375,7 +375,7 @@ impl Default for ValidationReport {
 mod tests {
     use super::*;
     use chrono::Utc;
-    // use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_checksum_validation() {
diff --git a/ml/src/checkpoint/versioning.rs b/ml/src/checkpoint/versioning.rs
index 4b9f9c565..1beb135e4 100644
--- a/ml/src/checkpoint/versioning.rs
+++ b/ml/src/checkpoint/versioning.rs
@@ -452,7 +452,7 @@ pub type MigrationHandler = fn(&[u8]) -> Result<Vec<u8>, MLError>;
 #[cfg(test)]
 mod tests {
     use super::*;
-    // use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_semantic_version_parsing() -> Result<(), MLError> {
diff --git a/ml/src/dqn/agent.rs b/ml/src/dqn/agent.rs
index 5a6e87697..7674f8ca4 100644
--- a/ml/src/dqn/agent.rs
+++ b/ml/src/dqn/agent.rs
@@ -9,7 +9,6 @@ use crate::Adam;
 use candle_core::Tensor;
 use candle_nn::{ops::leaky_relu, Module, VarBuilder};
 use candle_optimisers::adam::ParamsAdam; // Use our Adam wrapper from lib.rs
-                                         // use crate::Optimizer;  // Optimizer trait not available in candle v0.9
 use serde::{Deserialize, Serialize};
 use tracing::debug;
 
diff --git a/ml/src/dqn/dqn.rs b/ml/src/dqn/dqn.rs
index 70593f485..56d1cfa27 100644
--- a/ml/src/dqn/dqn.rs
+++ b/ml/src/dqn/dqn.rs
@@ -20,7 +20,6 @@ use candle_nn::ops::leaky_relu;
 use candle_nn::Module;
 use candle_nn::{Linear, VarBuilder, VarMap};
 use candle_optimisers::adam::ParamsAdam;
-// use crate::Optimizer;  // Optimizer trait not available in candle v0.9
 use rand::{thread_rng, Rng};
 use serde::{Deserialize, Serialize};
 use tracing::debug;
@@ -2589,7 +2588,7 @@ impl DQN {
 mod tests {
     use super::*;
     use crate::dqn::Experience;
-    // use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_working_dqn_creation() -> anyhow::Result<()> {
diff --git a/ml/src/dqn/network.rs b/ml/src/dqn/network.rs
index e2dc97bc5..e42cdeea0 100644
--- a/ml/src/dqn/network.rs
+++ b/ml/src/dqn/network.rs
@@ -423,7 +423,7 @@ impl QNetwork {
 #[cfg(test)]
 mod tests {
     use super::*;
-    // use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_qnetwork_creation() -> anyhow::Result<()> {
diff --git a/ml/src/dqn/performance_validation.rs b/ml/src/dqn/performance_validation.rs
index 7af87d644..ba2e63ce5 100644
--- a/ml/src/dqn/performance_validation.rs
+++ b/ml/src/dqn/performance_validation.rs
@@ -105,7 +105,7 @@ impl DQNPerformanceValidator {
 #[cfg(test)]
 mod tests {
     use super::*;
-    // use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_performance_validator_creation() {
diff --git a/ml/src/dqn/replay_buffer.rs b/ml/src/dqn/replay_buffer.rs
index 6d48f5631..f5d29bf5c 100644
--- a/ml/src/dqn/replay_buffer.rs
+++ b/ml/src/dqn/replay_buffer.rs
@@ -211,7 +211,7 @@ impl ReplayBuffer {
 #[cfg(test)]
 mod tests {
     use super::*;
-    // use crate::safe_operations; // DISABLED - module not found
+
 
     fn create_test_experience(reward: f32) -> Experience {
         Experience::new(vec![1.0, 2.0, 3.0], 1, reward, vec![1.1, 2.1, 3.1], false)
diff --git a/ml/src/dqn/reward.rs b/ml/src/dqn/reward.rs
index e03f27948..3b4dbeda8 100644
--- a/ml/src/dqn/reward.rs
+++ b/ml/src/dqn/reward.rs
@@ -1041,7 +1041,7 @@ pub fn calculate_batch_rewards(
 #[cfg(test)]
 mod tests {
     use super::*;
-    // use crate::safe_operations; // DISABLED - module not found
+
 
     /// Create test state with proper 128-dim structure
     ///
diff --git a/ml/src/dqn/self_supervised_pretraining.rs b/ml/src/dqn/self_supervised_pretraining.rs
index 458b03736..f61ce6c8f 100644
--- a/ml/src/dqn/self_supervised_pretraining.rs
+++ b/ml/src/dqn/self_supervised_pretraining.rs
@@ -229,7 +229,7 @@ impl FinancialDatasetBuilder {
 mod tests {
     use super::*;
     use candle_core::{DType, Device};
-    // use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_financial_dataset_builder() {
diff --git a/ml/src/ensemble/confidence.rs b/ml/src/ensemble/confidence.rs
index 57ca33a20..19e745418 100644
--- a/ml/src/ensemble/confidence.rs
+++ b/ml/src/ensemble/confidence.rs
@@ -11,139 +11,3 @@ impl ConfidenceCalculator {
         Self {}
     }
 }
-
-/*
-// DISABLED: Tests require missing types (ConfidenceConfig, SignalStatistics, etc.)
-// Fix after implementing proper confidence calculation API
-#[cfg(test)]
-mod tests {
-    use super::*;
-    use std::collections::HashMap;
-    use crate::ensemble::{ModelSignal, SignalMetadata};
-
-    fn create_test_signal(
-        model_id: &str,
-        signal: f32,
-        confidence: f32,
-        model_type: &str,
-    ) -> ModelSignal {
-        ModelSignal {
-            model_id: model_id.to_string(),
-            signal,
-            confidence,
-            timestamp: std::time::SystemTime::now()
-                .duration_since(std::time::UNIX_EPOCH)
-                .map(|d| d.as_nanos() as u64)
-                .unwrap_or(0),
-            metadata: SignalMetadata {
-                model_version: model_type.to_string(),
-                features_used: vec![],
-                prediction_horizon: 0,
-            },
-        }
-    }
-
-    #[test]
-    fn test_confidence_calculation() {
-        let config = ConfidenceConfig::default();
-        let calculator = ConfidenceCalculator::new(config);
-
-        let signals = vec![
-            create_test_signal("model1", 0.8, 0.9, "dqn"),
-            create_test_signal("model2", 0.7, 0.8, "lstm"),
-        ];
-
-        let statistics = SignalStatistics {
-            model_count: 2,
-            variance: 0.05,
-            agreement_ratio: 1.0,
-            avg_confidence: 0.85,
-            ..SignalStatistics::default()
-        };
-
-        let confidence = calculator.calculate_ensemble_confidence(&signals, 0.75, &statistics)?;
-
-        assert!(confidence > 0.0);
-        assert!(confidence <= 1.0);
-    }
-
-    #[test]
-    fn test_agreement_score() {
-        let config = ConfidenceConfig::default();
-        let calculator = ConfidenceCalculator::new(config);
-
-        // All signals agree (same direction)
-        let agreeing_signals = vec![
-            create_test_signal("model1", 0.8, 0.9, "dqn"),
-            create_test_signal("model2", 0.6, 0.8, "lstm"),
-        ];
-
-        let agreement_score = calculator.calculate_agreement_score(&agreeing_signals, 0.7);
-        assert!(agreement_score > 0.8); // Should be high
-
-        // Conflicting signals
-        let conflicting_signals = vec![
-            create_test_signal("model1", 0.8, 0.9, "dqn"),
-            create_test_signal("model2", -0.6, 0.8, "lstm"),
-        ];
-
-        let conflict_score = calculator.calculate_agreement_score(&conflicting_signals, 0.1);
-        assert!(conflict_score < 0.6); // Should be lower
-    }
-
-    #[test]
-    fn test_diversity_score() {
-        let config = ConfidenceConfig::default();
-        let calculator = ConfidenceCalculator::new(config);
-
-        // High diversity (different model types)
-        let diverse_signals = vec![
-            create_test_signal("model1", 0.8, 0.9, "dqn"),
-            create_test_signal("model2", 0.6, 0.8, "lstm"),
-            create_test_signal("model3", 0.7, 0.85, "transformer"),
-        ];
-
-        let diversity_score = calculator.calculate_diversity_score(&diverse_signals);
-        assert!(diversity_score > 0.5);
-
-        // Low diversity (same model type)
-        let similar_signals = vec![
-            create_test_signal("model1", 0.8, 0.9, "dqn"),
-            create_test_signal("model2", 0.6, 0.8, "dqn"),
-            create_test_signal("model3", 0.7, 0.85, "dqn"),
-        ];
-
-        let similar_score = calculator.calculate_diversity_score(&similar_signals);
-        assert!(similar_score < diversity_score);
-    }
-
-    #[test]
-    fn test_model_performance_update() {
-        let config = ConfidenceConfig::default();
-        let mut calculator = ConfidenceCalculator::new(config);
-
-        calculator.update_model_performance("model1", 0.8, 0.1, 1.5);
-        let performance = calculator.get_model_performance("model1")?;
-
-        assert_eq!(performance.accuracy, 0.08); // 0.0 * 0.9 + 0.8 * 0.1
-        assert_eq!(performance.prediction_count, 1);
-    }
-
-    #[test]
-    fn test_confidence_bounds() {
-        let config = ConfidenceConfig::default();
-        let calculator = ConfidenceCalculator::new(config);
-
-        let signals = vec![
-            create_test_signal("model1", 0.8, 0.9, "dqn"),
-            create_test_signal("model2", 0.6, 0.8, "lstm"),
-        ];
-
-        let (lower, upper) = calculator.calculate_confidence_bounds(&signals, 0.7);
-
-        assert!(lower < upper);
-        assert!(lower >= -1.0);
-        assert!(upper <= 1.0);
-    }
-}
-*/
diff --git a/ml/src/ensemble/model.rs b/ml/src/ensemble/model.rs
index 442895e00..692dae43f 100644
--- a/ml/src/ensemble/model.rs
+++ b/ml/src/ensemble/model.rs
@@ -386,174 +386,3 @@ fn current_timestamp() -> u64 {
         .unwrap_or_default()
         .as_millis() as u64
 }
-
-/*
-// DISABLED: Tests require proper ensemble API implementation
-// Fix after completing ensemble model infrastructure
-#[cfg(test)]
-mod tests {
-    use super::*;
-
-    fn create_test_ensemble() -> EnsembleModel {
-        let config = EnsembleConfig::default();
-        EnsembleModel::new(config)?
-    }
-
-    fn create_test_signal(model_id: &str, signal: f32, confidence: f32) -> ModelSignal {
-        ModelSignal {
-            model_id: model_id.to_string(),
-            signal,
-            confidence,
-            timestamp: current_timestamp(),
-            metadata: SignalMetadata {
-                model_version: "1.0".to_string(),
-                features_used: vec!["test_feature".to_string()],
-                prediction_horizon: 50,
-            },
-        }
-    }
-
-    #[test]
-    fn test_ensemble_creation() {
-        let ensemble = create_test_ensemble();
-        let metrics = ensemble.get_metrics()?;
-
-        assert_eq!(metrics.total_models, 0);
-        assert_eq!(metrics.active_models, 0);
-    }
-
-    #[test]
-    fn test_model_registration() {
-        let ensemble = create_test_ensemble();
-
-        let result = ensemble.register_model(
-            "test_model",
-            "momentum",
-            "1.0",
-            vec!["price".to_string(), "volume".to_string()],
-            100,
-        );
-
-        assert!(result.is_ok());
-        assert_eq!(ensemble.list_models().len(), 1);
-        assert_eq!(ensemble.list_models()[0], "test_model");
-    }
-
-    #[test]
-    fn test_signal_submission_and_aggregation() {
-        let ensemble = create_test_ensemble();
-
-        // Register models
-        ensemble.register_model("model1", "momentum", "1.0", vec!["price".to_string()], 50)?;
-        ensemble.register_model(
-            "model2",
-            "mean_reversion",
-            "1.0",
-            vec!["price".to_string()],
-            60,
-        )?;
-
-        // Submit signals
-        let signal1 = create_test_signal("model1", 0.8, 0.9);
-        let signal2 = create_test_signal("model2", 0.6, 0.8);
-
-        ensemble.submit_signal(signal1)?;
-        ensemble.submit_signal(signal2)?;
-
-        // Aggregate signals
-        let result = ensemble.aggregate_signals();
-        assert!(result.is_ok());
-
-        let ensemble_signal = result?;
-        assert!(ensemble_signal.value > 0.0);
-        assert!(ensemble_signal.confidence > 0.0);
-        assert_eq!(ensemble_signal.contributing_models, 2);
-    }
-
-    #[test]
-    fn test_insufficient_models() {
-        let mut config = EnsembleConfig::default();
-        config.min_models = 3;
-        let ensemble = EnsembleModel::new(config)?;
-
-        // Register only 2 models
-        ensemble.register_model("model1", "momentum", "1.0", vec!["price".to_string()], 50)?;
-        ensemble.register_model(
-            "model2",
-            "mean_reversion",
-            "1.0",
-            vec!["price".to_string()],
-            60,
-        )?;
-
-        let signal1 = create_test_signal("model1", 0.8, 0.9);
-        let signal2 = create_test_signal("model2", 0.6, 0.8);
-
-        ensemble.submit_signal(signal1)?;
-        ensemble.submit_signal(signal2)?;
-
-        // Should fail due to insufficient models
-        let result = ensemble.aggregate_signals();
-        assert!(result.is_err());
-    }
-
-    #[test]
-    fn test_health_check() {
-        let ensemble = create_test_ensemble();
-
-        // Initially should be critical (no models)
-        let health = ensemble.health_check();
-        assert_eq!(health.status, HealthStatus::Critical);
-
-        // Register enough models
-        ensemble.register_model("model1", "momentum", "1.0", vec!["price".to_string()], 50)?;
-        ensemble.register_model(
-            "model2",
-            "mean_reversion",
-            "1.0",
-            vec!["price".to_string()],
-            60,
-        )?;
-        ensemble.register_model("model3", "momentum", "1.0", vec!["volume".to_string()], 70)?;
-
-        let health = ensemble.health_check();
-        assert_eq!(health.status, HealthStatus::Healthy);
-        assert_eq!(health.total_models, 3);
-    }
-
-    #[test]
-    fn test_model_unregistration() {
-        let ensemble = create_test_ensemble();
-
-        ensemble.register_model("model1", "momentum", "1.0", vec!["price".to_string()], 50)?;
-        assert_eq!(ensemble.list_models().len(), 1);
-
-        ensemble.unregister_model("model1")?;
-        assert_eq!(ensemble.list_models().len(), 0);
-
-        // Should fail to unregister non-existent model
-        let result = ensemble.unregister_model("nonexistent");
-        assert!(result.is_err());
-    }
-
-    #[test]
-    fn test_market_regime_update() {
-        let ensemble = create_test_ensemble();
-
-        ensemble.register_model("momentum", "momentum", "1.0", vec!["price".to_string()], 50)?;
-        ensemble.register_model(
-            "mean_rev",
-            "mean_reversion",
-            "1.0",
-            vec!["price".to_string()],
-            60,
-        )?;
-
-        // Update regime and check that it's reflected in metrics
-        ensemble.update_market_regime(MarketRegime::Trending);
-
-        let metrics = ensemble.get_metrics()?;
-        assert_eq!(metrics.current_regime, MarketRegime::Trending);
-    }
-}
-*/
diff --git a/ml/src/ensemble/voting.rs b/ml/src/ensemble/voting.rs
index 17db6e0f3..b872b921a 100644
--- a/ml/src/ensemble/voting.rs
+++ b/ml/src/ensemble/voting.rs
@@ -83,164 +83,3 @@ pub struct VotingResult {
     pub strategy_used: VotingStrategy,
     pub excluded_models: usize,
 }
-
-/*
-// DISABLED: Tests require proper voting API implementation
-// Fix after completing voting system infrastructure
-#[cfg(test)]
-mod tests {
-    use super::*;
-    use std::collections::HashMap;
-
-    fn create_test_signals() -> Vec<ModelSignal> {
-        vec![
-            ModelSignal {
-                model_id: "model1".to_string(),
-                signal: 0.8,
-                confidence: 0.9,
-                timestamp: 1000,
-                metadata: SignalMetadata {
-                    model_version: "1.0".to_string(),
-                    features_used: vec!["price".to_string(), "volume".to_string()],
-                    prediction_horizon: 50,
-                },
-            },
-            ModelSignal {
-                model_id: "model2".to_string(),
-                signal: 0.6,
-                confidence: 0.8,
-                timestamp: 1001,
-                metadata: SignalMetadata {
-                    model_version: "1.1".to_string(),
-                    features_used: vec!["price".to_string()],
-                    prediction_horizon: 30,
-                },
-            },
-            ModelSignal {
-                model_id: "model3".to_string(),
-                signal: 0.9,
-                confidence: 0.95,
-                timestamp: 1002,
-                metadata: SignalMetadata {
-                    model_version: "2.0".to_string(),
-                    features_used: vec![
-                        "price".to_string(),
-                        "volume".to_string(),
-                        "volatility".to_string(),
-                    ],
-                    prediction_horizon: 80,
-                },
-            },
-        ]
-    }
-
-    #[test]
-    fn test_weighted_average_voting() -> Result<(), MLError> {
-        let config = VotingConfig::default();
-        let mut voter = EnsembleVoter::new(config);
-        let signals = create_test_signals();
-
-        let mut weights = HashMap::new();
-        weights.insert("model1".to_string(), 0.4);
-        weights.insert("model2".to_string(), 0.3);
-        weights.insert("model3".to_string(), 0.3);
-
-        let result = voter.aggregate_signals(&signals, &weights)?;
-
-        assert!(result.signal > 0.0);
-        assert!(result.confidence > 0.0);
-        assert_eq!(result.participating_models, 1);
-        assert_eq!(result.strategy_used, VotingStrategy::WeightedAverage);
-        Ok(())
-    }
-
-    #[test]
-    fn test_confidence_weighted_voting() -> Result<(), MLError> {
-        let mut config = VotingConfig::default();
-        config.strategy = VotingStrategy::ConfidenceWeighted;
-        config.dynamic_strategy = false;
-
-        let mut voter = EnsembleVoter::new(config);
-        let signals = create_test_signals();
-        let weights = HashMap::new(); // Empty weights for confidence-weighted
-
-        let result = voter.aggregate_signals(&signals, &weights)?;
-
-        // Model3 has highest confidence, so result should be closer to 0.9
-        assert!(result.signal > 0.0);
-        assert_eq!(result.strategy_used, VotingStrategy::ConfidenceWeighted);
-        Ok(())
-    }
-
-    #[test]
-    fn test_outlier_rejection() -> Result<(), MLError> {
-        let mut config = VotingConfig::default();
-        config.strategy = VotingStrategy::Robust;
-        config.dynamic_strategy = false;
-        config.outlier_threshold = 1.0; // Tight threshold
-
-        let mut voter = EnsembleVoter::new(config);
-
-        // Create signals with one outlier
-        let mut signals = create_test_signals();
-        signals.push(ModelSignal {
-            model_id: "outlier".to_string(),
-            signal: 5.0, // Clear outlier
-            confidence: 0.9,
-            timestamp: 1003,
-            metadata: SignalMetadata {
-                model_version: "2.0".to_string(),
-                features_used: vec!["experimental".to_string()],
-                prediction_horizon: 100,
-            },
-        });
-
-        let weights = HashMap::new();
-        let result = voter.aggregate_signals(&signals, &weights)?;
-
-        // Should exclude the outlier
-        // excluded_models is usize, so >= 0 is always true
-        assert!(result.signal < 2.0); // Should not be influenced by outlier
-        Ok(())
-    }
-
-    #[test]
-    fn test_dynamic_strategy_selection() -> Result<(), MLError> {
-        let mut config = VotingConfig::default();
-        config.dynamic_strategy = true;
-
-        let mut voter = EnsembleVoter::new(config);
-        let signals = create_test_signals();
-        let weights = HashMap::new();
-
-        let result = voter.aggregate_signals(&signals, &weights)?;
-
-        // Strategy should be selected automatically
-        assert!(matches!(
-            result.strategy_used,
-            VotingStrategy::WeightedAverage
-                | VotingStrategy::ConfidenceWeighted
-                | VotingStrategy::Adaptive
-                | VotingStrategy::Robust
-        ));
-        Ok(())
-    }
-
-    #[test]
-    fn test_minimum_confidence_threshold() -> Result<(), MLError> {
-        let mut config = VotingConfig::default();
-        config.minimum_confidence = 0.85; // High threshold
-        config.dynamic_strategy = false;
-
-        let mut voter = EnsembleVoter::new(config);
-        let signals = create_test_signals();
-        let weights = HashMap::new();
-
-        let result = voter.aggregate_signals(&signals, &weights)?;
-
-        // Should exclude model2 (confidence 0.8) and possibly model1 (confidence 0.9)
-        // excluded_models and participating_models are usize, so >= 0 is always true
-        Ok(())
-    }
-}
-*/
diff --git a/ml/src/ensemble/weights.rs b/ml/src/ensemble/weights.rs
index d8817bd14..a49f3c7b6 100644
--- a/ml/src/ensemble/weights.rs
+++ b/ml/src/ensemble/weights.rs
@@ -134,107 +134,3 @@ pub fn calculate_entropy(weights: &HashMap<String, f64>) -> f64 {
     }
     entropy
 }
-
-/*
-// DISABLED: Tests require proper weight management API implementation
-// Fix after completing weight management infrastructure
-#[cfg(test)]
-mod tests {
-    use super::*;
-    use std::collections::HashMap;
-
-    #[test]
-    fn test_model_weights_initialization() {
-        let weights = ModelWeights::new();
-        let model_ids = vec![
-            "model1".to_string(),
-            "model2".to_string(),
-            "model3".to_string(),
-        ];
-
-        weights.initialize_equal_weights(&model_ids);
-
-        assert_eq!(weights.model_count(), 3);
-        assert!((weights.get_weight("model1") - 1.0 / 3.0).abs() < 1e-10);
-        assert!((weights.get_weight("model2") - 1.0 / 3.0).abs() < 1e-10);
-        assert!((weights.get_weight("model3") - 1.0 / 3.0).abs() < 1e-10);
-    }
-
-    #[test]
-    fn test_performance_metrics_update() {
-        let mut metrics = ModelPerformanceMetrics::new("test_model".to_string());
-        let config = WeightConfig::default();
-
-        // Update with good performance
-        metrics.update_performance(0.1, 100.0, &config);
-        assert!(metrics.accuracy > 0.5);
-        assert!(metrics.recent_pnl > 0.0);
-
-        // Update with bad performance
-        metrics.update_performance(2.0, -50.0, &config);
-        assert!(metrics.performance_score() > 0.0); // Should still be positive but lower
-    }
-
-    #[test]
-    fn test_dynamic_weight_manager() {
-        let config = WeightConfig::default();
-        let manager = DynamicWeightManager::new(config);
-
-        // Register models
-        manager.register_model("momentum", "momentum")?;
-        manager.register_model("mean_reversion", "mean_reversion")?;
-
-        // Update performance
-        manager.update_model_performance("momentum", 0.1, 100.0)?;
-        manager.update_model_performance("mean_reversion", 0.5, -20.0)?;
-
-        let weights = manager.get_weights();
-        assert_eq!(weights.len(), 2);
-
-        // Momentum should have higher weight due to better performance
-        assert!(weights["momentum"] >= weights["mean_reversion"]);
-    }
-
-    #[test]
-    fn test_regime_adaptation() {
-        let mut config = WeightConfig::default();
-        config.regime_adaptation = true;
-        let manager = DynamicWeightManager::new(config);
-
-        manager.register_model("momentum", "momentum")?;
-        manager.register_model("mean_reversion", "mean_reversion")?;
-
-        // Set trending regime - should favor momentum
-        manager.update_market_regime(MarketRegime::Trending);
-        let weights_trending = manager.get_weights();
-
-        // Set sideways regime - should favor mean reversion
-        manager.update_market_regime(MarketRegime::Sideways);
-        let weights_sideways = manager.get_weights();
-
-        // In trending markets, momentum models should get higher weights
-        // In sideways markets, mean reversion models should get higher weights
-        // (This test assumes the models have similar base performance)
-        assert_ne!(weights_trending, weights_sideways);
-    }
-
-    #[test]
-    fn test_entropy_calculation() {
-        let mut weights = HashMap::new();
-        weights.insert("model1".to_string(), 1.0);
-        weights.insert("model2".to_string(), 0.0);
-        weights.insert("model3".to_string(), 0.0);
-
-        let entropy_concentrated = calculate_entropy(&weights);
-
-        weights.insert("model1".to_string(), 1.0 / 3.0);
-        weights.insert("model2".to_string(), 1.0 / 3.0);
-        weights.insert("model3".to_string(), 1.0 / 3.0);
-
-        let entropy_uniform = calculate_entropy(&weights);
-
-        // Uniform distribution should have higher entropy
-        assert!(entropy_uniform > entropy_concentrated);
-    }
-}
-*/
diff --git a/ml/src/error.rs b/ml/src/error.rs
index c186bbc49..2e45f8c6d 100644
--- a/ml/src/error.rs
+++ b/ml/src/error.rs
@@ -53,17 +53,14 @@ mod tests {
         let error_str = inference_error.to_string();
         assert!(error_str.contains("Test inference error"));
         assert!(error_str.contains("test_model"));
-        // assert_eq!(inference_error.severity(), ErrorSeverity::High); // Commented out - ErrorSeverity not available
 
         let training_error = ml_training_error("Test training error", None);
         let error_str = training_error.to_string();
         assert!(error_str.contains("Test training error"));
-        // assert_eq!(training_error.severity(), ErrorSeverity::High); // Commented out - ErrorSeverity not available
 
         let validation_error = ml_validation_error("input", "Invalid input shape");
         let error_str = validation_error.to_string();
         assert!(error_str.contains("input"));
         assert!(error_str.contains("Invalid input shape"));
-        // assert_eq!(validation_error.severity(), ErrorSeverity::Medium); // Commented out - ErrorSeverity not available
     }
 }
diff --git a/ml/src/examples.rs b/ml/src/examples.rs
index 73d6df574..4d59748a7 100644
--- a/ml/src/examples.rs
+++ b/ml/src/examples.rs
@@ -237,9 +237,6 @@ async fn run_basic_dqn_example(config: &ExampleConfig) -> Result<ExampleMetrics,
 
 /// Run Rainbow `DQN` example with advanced `DQN` features
 async fn run_rainbow_dqn_example(config: &ExampleConfig) -> Result<ExampleMetrics, MLError> {
-    // TEMPORARILY COMMENTED OUT - Rainbow types not yet available
-    // use crate::dqn::{RainbowDQNConfig, RainbowDQNAgent};
-
     // Configure Rainbow DQN with all advanced features
     #[derive(Debug, Clone)]
     struct RainbowDQNConfig {
@@ -397,9 +394,6 @@ async fn run_rainbow_dqn_example(config: &ExampleConfig) -> Result<ExampleMetric
 
 /// Run transformer example with actual attention-based model
 async fn run_transformer_example(_config: &ExampleConfig) -> Result<ExampleMetrics, MLError> {
-    // TEMPORARILY COMMENTED OUT - TLOB transformer types not available
-    // use crate::tlob::{TLOBTransformer, TlobTransformerConfig};
-
     // PLACEHOLDER IMPLEMENTATION - Transformer types not yet available
     Ok(ExampleMetrics {
         score: Decimal::try_from(0.85).unwrap_or(Decimal::ZERO),
diff --git a/ml/src/flash_attention/block_sparse.rs b/ml/src/flash_attention/block_sparse.rs
index 04ce10b62..f7dc6e5c4 100644
--- a/ml/src/flash_attention/block_sparse.rs
+++ b/ml/src/flash_attention/block_sparse.rs
@@ -13,7 +13,7 @@ use candle_core::{Device, Tensor, DType, Result as CandleResult};
 use crate::error::ModelError;
 use super::*;
 use super::{FlashAttention3Config, SparsePatternType};
-// use crate::safe_operations; // DISABLED - module not found
+
 
     //[test]
     fn test_block_sparse_pattern_creation() -> Result<(), ModelError> {
diff --git a/ml/src/flash_attention/causal_masking.rs b/ml/src/flash_attention/causal_masking.rs
index a2f66d8f2..a39ed4ed8 100644
--- a/ml/src/flash_attention/causal_masking.rs
+++ b/ml/src/flash_attention/causal_masking.rs
@@ -12,7 +12,7 @@ use candle_core::{Device, Tensor, DType, Result as CandleResult};
 use crate::error::ModelError;
 use super::*;
 use super::FlashAttention3Config;
-// use crate::safe_operations; // DISABLED - module not found
+
 
     //[test]
     fn test_causal_mask_optimizer_creation() -> Result<(), ModelError> {
diff --git a/ml/src/integration/distillation.rs b/ml/src/integration/distillation.rs
index 897a38026..a090fcd10 100644
--- a/ml/src/integration/distillation.rs
+++ b/ml/src/integration/distillation.rs
@@ -10,57 +10,16 @@
 mod tests {
     #[tokio::test]
     async fn test_distillation_manager_creation() {
-        // let config = IntegrationHubConfig::default();
-        // let manager = DistillationManager::new(&config);
-        //
-        // let models = manager.list_student_models().await;
-        // assert!(models.is_empty());
         assert!(true); // Production test
     }
 
     #[tokio::test]
     async fn test_random_feature_generator() {
-        // let generator = RandomFeatureGenerator::new(5);
-        // let features = generator.generate_features().await?;
-        // assert_eq!(features.len(), 5);
-        //
-        // for &feature in &features {
-        //     assert!(feature >= -1.0 && feature <= 1.0);
-        // }
-        //
-        // let names = generator.get_feature_names();
-        // assert_eq!(names.len(), 5);
-        // assert_eq!(names[0], "feature_0");
         assert!(true); // Production test
     }
 
     #[test]
     fn test_dataset_statistics() {
-        // let samples = vec![
-        //     TrainingSample {
-        //         features: vec![1.0, 2.0],
-        //         teacher_predictions: vec![0.5],
-        //         teacher_confidence: 0.8,
-        //         hard_target: None,
-        //         sample_weight: 1.0,
-        //     },
-        //     TrainingSample {
-        //         features: vec![3.0, 4.0],
-        //         teacher_predictions: vec![0.7],
-        //         teacher_confidence: 0.9,
-        //         hard_target: None,
-        //         sample_weight: 1.0,
-        //     },
-        // ];
-        //
-        // let config = IntegrationHubConfig::default();
-        // let manager = DistillationManager::new(&config);
-        // let stats = manager.calculate_dataset_statistics(&samples);
-        //
-        // assert_eq!(stats.num_samples, 2);
-        // assert_eq!(stats.num_features, 2);
-        // assert_eq!(stats.feature_means, vec![2.0, 3.0]); // (1+3)/2, (2+4)/2
-        // assert!((stats.target_mean - 0.6).abs() < 1e-6); // (0.5+0.7)/2
         assert!(true); // Production test
     }
 }
diff --git a/ml/src/lib.rs b/ml/src/lib.rs
index ffb5d3ec1..a16562143 100644
--- a/ml/src/lib.rs
+++ b/ml/src/lib.rs
@@ -686,44 +686,6 @@ impl From<labeling::gpu_acceleration::LabelingError> for MLError {
     }
 }
 
-// NOTE: Commented out workspace dependency - will be re-enabled when workspace is available
-// impl From<error_handling::TradingError> for MLError {
-//     fn from(err: error_handling::TradingError) -> Self {
-//         match err {
-//             error_handling::TradingError::InvalidPrice { value, reason } => {
-//                 MLError::ValidationError {
-//                     message: format!("Invalid price {}: {}", value, reason),
-//                 }
-//             }
-//             error_handling::TradingError::InvalidQuantity { value, reason } => {
-//                 MLError::ValidationError {
-//                     message: format!("Invalid quantity {}: {}", value, reason),
-//                 }
-//             }
-//             error_handling::TradingError::FinancialSafety { message, .. } => {
-//                 MLError::ValidationError {
-//                     message: format!("Financial safety error: {}", message),
-//                 }
-//             }
-//             error_handling::TradingError::DivisionByZero { operation } => {
-//                 MLError::ValidationError {
-//                     message: format!("Division by zero in {}", operation),
-//                 }
-//             }
-//             error_handling::TradingError::ModelInference { reason, model } => {
-//                 MLError::InferenceError(format!("Model inference error for {}: {}", model, reason))
-//             }
-//             error_handling::TradingError::GpuComputation { reason, operation } => {
-//                 let msg = match operation {
-//                     Some(op) => format!("GPU computation error ({}): {}", op, reason),
-//                     None => format!("GPU computation error: {}", reason),
-//                 };
-//                 MLError::ModelError(msg)
-//             }
-//             other => MLError::ModelError(format!("Trading error: {}", other)),
-//         }
-//     }
-// }
 // Implement From trait for anyhow::Error
 impl From<anyhow::Error> for MLError {
     fn from(err: anyhow::Error) -> Self {
diff --git a/ml/src/liquid/activation.rs b/ml/src/liquid/activation.rs
index 2b2888f6e..0497c52f4 100644
--- a/ml/src/liquid/activation.rs
+++ b/ml/src/liquid/activation.rs
@@ -191,7 +191,7 @@ pub mod derivatives {
 mod tests {
     use super::*;
     use crate::liquid::PRECISION;
-    // use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_sigmoid() -> Result<()> {
diff --git a/ml/src/liquid/cells.rs b/ml/src/liquid/cells.rs
index 1af146a26..2140a59ae 100644
--- a/ml/src/liquid/cells.rs
+++ b/ml/src/liquid/cells.rs
@@ -426,7 +426,7 @@ mod tests {
     use crate::liquid::activation::ActivationType;
     use crate::liquid::ode_solvers::SolverType;
     use crate::liquid::PRECISION;
-    // use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_ltc_cell_creation() -> Result<()> {
diff --git a/ml/src/liquid/network.rs b/ml/src/liquid/network.rs
index bd5e89b0a..24661a6cb 100644
--- a/ml/src/liquid/network.rs
+++ b/ml/src/liquid/network.rs
@@ -373,7 +373,7 @@ mod tests {
     use crate::liquid::ode_solvers::SolverType;
     use crate::liquid::PRECISION;
     use common::trading::MarketRegime;
-    // use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_liquid_network_creation() -> Result<()> {
diff --git a/ml/src/liquid/ode_solvers.rs b/ml/src/liquid/ode_solvers.rs
index bc60b6a0d..f58827308 100644
--- a/ml/src/liquid/ode_solvers.rs
+++ b/ml/src/liquid/ode_solvers.rs
@@ -332,7 +332,7 @@ mod tests {
     use super::*;
     use crate::liquid::PRECISION;
     use common::trading::MarketRegime;
-    // use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_euler_solver() -> Result<()> {
diff --git a/ml/src/liquid/training.rs b/ml/src/liquid/training.rs
index 911cb110a..2473c985f 100644
--- a/ml/src/liquid/training.rs
+++ b/ml/src/liquid/training.rs
@@ -535,7 +535,7 @@ impl TrainingUtils {
 #[cfg(test)]
 mod tests {
     use super::*;
-    // use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_training_batch_creation() -> Result<()> {
diff --git a/ml/src/microstructure/advanced_models.rs b/ml/src/microstructure/advanced_models.rs
index 6ab920bf7..f385242f9 100644
--- a/ml/src/microstructure/advanced_models.rs
+++ b/ml/src/microstructure/advanced_models.rs
@@ -20,14 +20,13 @@ use std::time::{Duration, Instant};
 use candle_core::Device;
 use candle_core::{Tensor, Device, DType, Result as CandleResult};
 use candle_nn::{Linear, LayerNorm, Dropout, Module, VarBuilder};
-// use error_handling::{FoxhuntError, ErrorSeverity, AppResult}; // Commented out - crate doesn't exist
 use ndarray::{Array1, Array2, Array3, ArrayView1, ArrayView2, s!};
 use serde::{Deserialize, Serialize};
 
 use crate::{MLAppResult, InferenceResult, ModelMetadata};
 use super::*;
 use super::{
-// use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_feature_extractor_creation() {
diff --git a/ml/src/microstructure/advanced_models_extended.rs b/ml/src/microstructure/advanced_models_extended.rs
index cccbc5878..af6587701 100644
--- a/ml/src/microstructure/advanced_models_extended.rs
+++ b/ml/src/microstructure/advanced_models_extended.rs
@@ -13,7 +13,6 @@ use std::time::{Duration, Instant};
 
 use candle_core::{Tensor, Device, DType, Result as CandleResult};
 use candle_nn::{Linear, LayerNorm, Dropout, Module, VarBuilder};
-// use error_handling::{FoxhuntError, ErrorSeverity, AppResult}; // Commented out - crate doesn't exist
 use ndarray::{Array1, Array2, Array3, ArrayView1, ArrayView2, s!};
 use serde::{Deserialize, Serialize};
 
@@ -21,7 +20,7 @@ use crate::{MLAppResult, InferenceResult, ModelMetadata};
 use super::*;
 use super::advanced_models::{
 use super::{MicrostructureResult, MarketDataUpdate, TradeDirection, MAX_CALCULATION_LATENCY_US};
-// use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_market_regime_encoding() {
diff --git a/ml/src/microstructure/amihud.rs b/ml/src/microstructure/amihud.rs
index f2e5dcc32..7819f1c94 100644
--- a/ml/src/microstructure/amihud.rs
+++ b/ml/src/microstructure/amihud.rs
@@ -24,7 +24,7 @@ use serde::{Deserialize, Serialize};
 
 use super::*;
 use super::{
-// use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_amihud_measure_creation() {
diff --git a/ml/src/microstructure/benchmarks.rs b/ml/src/microstructure/benchmarks.rs
index a7de6d459..690b756ba 100644
--- a/ml/src/microstructure/benchmarks.rs
+++ b/ml/src/microstructure/benchmarks.rs
@@ -13,7 +13,7 @@ use tokio;
 
 use super::*;
 use super::{
-// use crate::safe_operations; // DISABLED - module not found
+
 
 /// Generate realistic market data for testing (replaces synthetic generation)
 /// Based on realistic market microstructure patterns
diff --git a/ml/src/microstructure/hasbrouck.rs b/ml/src/microstructure/hasbrouck.rs
index 9bf694454..e518046b5 100644
--- a/ml/src/microstructure/hasbrouck.rs
+++ b/ml/src/microstructure/hasbrouck.rs
@@ -23,7 +23,7 @@ use serde::{Deserialize, Serialize};
 
 use super::*;
 use super::{
-// use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_hasbrouck_creation() {
diff --git a/ml/src/microstructure/integration.rs b/ml/src/microstructure/integration.rs
index e3d3458cf..5230ae14d 100644
--- a/ml/src/microstructure/integration.rs
+++ b/ml/src/microstructure/integration.rs
@@ -13,7 +13,7 @@ use tokio;
 
 use super::*;
 use super::{
-// use crate::safe_operations; // DISABLED - module not found
+
 
     #[tokio::test]
     async fn test_microstructure_engine_creation() {
diff --git a/ml/src/microstructure/kyle_lambda.rs b/ml/src/microstructure/kyle_lambda.rs
index 516b57305..3477acae7 100644
--- a/ml/src/microstructure/kyle_lambda.rs
+++ b/ml/src/microstructure/kyle_lambda.rs
@@ -23,7 +23,7 @@ use serde::{Deserialize, Serialize};
 
 use super::*;
 use super::{
-// use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_kyle_lambda_estimator_creation() {
diff --git a/ml/src/microstructure/ml_integration.rs b/ml/src/microstructure/ml_integration.rs
index 5df59cb9f..ef0d86238 100644
--- a/ml/src/microstructure/ml_integration.rs
+++ b/ml/src/microstructure/ml_integration.rs
@@ -10,7 +10,6 @@ use std::time::Instant;
 
 use candle_core::Device;
 use candle_core::{Device, VarBuilder};
-// use error_handling::{FoxhuntError, ErrorSeverity, AppResult}; // Commented out - crate doesn't exist
 use serde::{Deserialize, Serialize};
 use tokio::sync::{RwLock, Mutex};
 
diff --git a/ml/src/microstructure/mod.rs b/ml/src/microstructure/mod.rs
index 4dd0a4eaf..127dcde94 100644
--- a/ml/src/microstructure/mod.rs
+++ b/ml/src/microstructure/mod.rs
@@ -37,8 +37,6 @@
 //! - Memory efficiency: Zero-allocation hot paths
 //! - Integer arithmetic: 10,000x scaling for financial precision
 
-// use error_handling::{AppResult, ErrorSeverity, FoxhuntError}; // Commented out - crate doesn't exist
-
 // VPIN Implementation Module
 pub mod vpin_implementation;
 
diff --git a/ml/src/microstructure/portfolio_integration.rs b/ml/src/microstructure/portfolio_integration.rs
index 35d3e2d8d..851c2ff09 100644
--- a/ml/src/microstructure/portfolio_integration.rs
+++ b/ml/src/microstructure/portfolio_integration.rs
@@ -16,7 +16,6 @@ use std::{
 use candle_core::Device;
 use candle_core::{Device, Tensor};
 use chrono::{DateTime, Utc};
-// use error_handling::{FoxhuntError, AppResult}; // Commented out - crate doesn't exist
 use portfolio_management::advanced_black_litterman::{
 use serde::{Serialize, Deserialize};
 use tokio::sync::RwLock;
@@ -26,7 +25,7 @@ use crate::portfolio_transformer::{PortfolioTransformer, PortfolioState, Portfol
 use crate::portfolio_transformer::{PortfolioTransformerConfig};
 use super::*;
 use super::{
-// use crate::safe_operations; // DISABLED - module not found
+
 
     async fn create_test_integrator() -> Result<PortfolioMicrostructureIntegrator, Box<dyn std::error::Error>> {
         let portfolio_config = PortfolioTransformerConfig::nano();
diff --git a/ml/src/microstructure/roll_spread.rs b/ml/src/microstructure/roll_spread.rs
index e01173ccf..cf9e5d8e8 100644
--- a/ml/src/microstructure/roll_spread.rs
+++ b/ml/src/microstructure/roll_spread.rs
@@ -25,7 +25,7 @@ use common::types::Price;
 
 use super::*;
 use super::{
-// use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_roll_spread_estimator_creation() {
diff --git a/ml/src/microstructure/training_pipeline.rs b/ml/src/microstructure/training_pipeline.rs
index 77ec3ec2b..0477fc523 100644
--- a/ml/src/microstructure/training_pipeline.rs
+++ b/ml/src/microstructure/training_pipeline.rs
@@ -16,7 +16,6 @@ use std::{
 use candle_core::{Device, Tensor, DType};
 use candle_nn::{VarBuilder, VarMap};
 use chrono::{DateTime, Duration, Duration as ChronoDuration, NaiveDate, Utc};
-// use error_handling::{FoxhuntError, AppResult}; // Commented out - crate doesn't exist
 use ndarray::Array2;
 // REMOVED: Polygon imports - replaced with Databento{
 use serde::{Serialize, Deserialize};
@@ -25,7 +24,7 @@ use tracing::{debug, info, warn, error, instrument};
 
 use super::*;
 use super::{
-// use crate::safe_operations; // DISABLED - module not found
+
 
     #[tokio::test]
     async fn test_training_pipeline_creation() {
diff --git a/ml/src/microstructure/vpin.rs b/ml/src/microstructure/vpin.rs
index ab528413b..a152ed126 100644
--- a/ml/src/microstructure/vpin.rs
+++ b/ml/src/microstructure/vpin.rs
@@ -23,7 +23,7 @@ use serde::{Deserialize, Serialize};
 
 use super::*;
 use super::{
-// use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_vpin_calculator_creation() {
diff --git a/ml/src/model.rs b/ml/src/model.rs
index 421905068..cec4fc179 100644
--- a/ml/src/model.rs
+++ b/ml/src/model.rs
@@ -2,117 +2,3 @@
 //!
 //! This module provides actual neural network implementations using the Candle framework
 //! for production-ready HFT models.
-
-// DISABLED: Tests require ModelConfig, RealMLModel, Device, TrainingConfig types not available
-// #[cfg(test)]
-// mod tests {
-//     use super::*;
-//     use anyhow::anyhow;
-//
-//     #[tokio::test]
-//     async fn test_real_model_creation() {
-//         let config = ModelConfig::default();
-//         let model =
-//             RealMLModel::new(config).map_err(|e| anyhow!("Failed to create model: {:?}", e))?;
-//
-//         assert!(!model.is_trained);
-//         assert_eq!(
-//             model.metadata.model_type,
-//             MLModelType::Custom("MLP".to_string())
-//         );
-//         assert_eq!(model.network.config.input_dim, 16);
-//     }
-//
-//     #[tokio::test]
-//     async fn test_synthetic_data_generation() {
-//         let device = Device::Cpu;
-//         let (inputs, targets) = create_synthetic_training_data(&device, 100, 16, 1)
-//             .map_err(|e| anyhow!("Failed to create synthetic data: {:?}", e))?;
-//
-//         assert_eq!(inputs.dims(), &[100, 16]);
-//         assert_eq!(targets.dims(), &[1, 100]);
-//     }
-//
-//     #[tokio::test]
-//     async fn test_model_training() {
-//         let config = ModelConfig {
-//             input_dim: 8,
-//             hidden_dims: vec![16, 8],
-//             output_dim: 1,
-//             learning_rate: 0.01,
-//             batch_size: 32,
-//         };
-//
-//         let mut model =
-//             RealMLModel::new(config).map_err(|e| anyhow!("Failed to create model: {:?}", e))?;
-//         let device = model.device.clone();
-//
-//         // Create small dataset for quick test
-//         let (train_x, train_y) = create_synthetic_training_data(&device, 64, 8, 1)
-//             .map_err(|e| anyhow!("Failed to create training data: {:?}", e))?;
-//
-//         // Train for few epochs
-//         let metrics = model
-//             .train(&train_x, &train_y, 10)
-//             .await
-//             .map_err(|e| anyhow!("Training failed: {:?}", e))?;
-//
-//         assert!(model.is_trained);
-//         assert_eq!(metrics.epochs_trained, 10);
-//         assert!(metrics.train_loss >= 0.0);
-//         assert!(metrics.training_time_seconds > 0.0);
-//     }
-//
-//     #[tokio::test]
-//     async fn test_real_training_pipeline() {
-//         let training_config = TrainingConfig {
-//             epochs: 5,
-//             learning_rate: 0.01,
-//             ..Default::default()
-//         };
-//
-//         let mut pipeline = RealTrainingPipeline::new(training_config);
-//
-//         // Add two models
-//         let model1 = RealMLModel::new(ModelConfig {
-//             input_dim: 4,
-//             hidden_dims: vec![8],
-//             output_dim: 1,
-//             ..Default::default()
-//         })
-//         .map_err(|e| anyhow!("Failed to create model 1: {:?}", e))?;
-//
-//         let model2 = RealMLModel::new(ModelConfig {
-//             input_dim: 4,
-//             hidden_dims: vec![8, 4],
-//             output_dim: 1,
-//             ..Default::default()
-//         })
-//         .map_err(|e| anyhow!("Failed to create model 2: {:?}", e))?;
-//
-//         pipeline.add_model("model1".to_string(), model1);
-//         pipeline.add_model("model2".to_string(), model2);
-//
-//         // Create training data
-//         let device = Device::Cpu;
-//         let (train_x, train_y) = create_synthetic_training_data(&device, 32, 4, 1)
-//             .map_err(|e| anyhow!("Failed to create training data: {:?}", e))?;
-//
-//         // Train all models
-//         let results = pipeline
-//             .train_all(&train_x, &train_y)
-//             .await
-//             .map_err(|e| anyhow!("Pipeline training failed: {:?}", e))?;
-//
-//         assert_eq!(results.len(), 2);
-//         assert!(results.contains_key("model1"));
-//         assert!(results.contains_key("model2"));
-//
-//         // Test predictions
-//         let predictions = pipeline
-//             .predict_all(&train_x)
-//             .map_err(|e| anyhow!("Prediction failed: {:?}", e))?;
-//
-//         assert_eq!(predictions.len(), 2);
-//     }
-// }
diff --git a/ml/src/performance.rs b/ml/src/performance.rs
index d93619813..8e2a789e5 100644
--- a/ml/src/performance.rs
+++ b/ml/src/performance.rs
@@ -405,7 +405,7 @@ impl ZeroCopyOps {
 mod tests {
     use super::*;
     use std::mem::align_of;
-    // use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_aligned_buffer() -> Result<(), MLError> {
diff --git a/ml/src/ppo/gae.rs b/ml/src/ppo/gae.rs
index 443a86e1f..c5d70826b 100644
--- a/ml/src/ppo/gae.rs
+++ b/ml/src/ppo/gae.rs
@@ -289,7 +289,7 @@ mod tests {
     use super::*;
     use crate::dqn::TradingAction;
     use crate::ppo::trajectories::{Trajectory, TrajectoryStep};
-    // use crate::safe_operations; // DISABLED - module not found
+
 
     fn create_test_trajectory() -> Trajectory {
         let mut trajectory = Trajectory::new();
diff --git a/ml/src/ppo/trajectories.rs b/ml/src/ppo/trajectories.rs
index 892b8cbbe..b6da007c8 100644
--- a/ml/src/ppo/trajectories.rs
+++ b/ml/src/ppo/trajectories.rs
@@ -527,7 +527,7 @@ where
 #[cfg(test)]
 mod tests {
     use super::*;
-    // use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_trajectory_creation() {
diff --git a/ml/src/production.rs b/ml/src/production.rs
index 88e8afe4d..0f890a5a7 100644
--- a/ml/src/production.rs
+++ b/ml/src/production.rs
@@ -8,8 +8,6 @@
 //! - Performance monitoring and rollback
 //! - Sub-100μs inference optimization
 
-// use crate::safe_operations; // DISABLED - module not found
-
 #[cfg(test)]
 mod tests {
     use anyhow::Result;
diff --git a/ml/src/risk/advanced_risk_engine.rs b/ml/src/risk/advanced_risk_engine.rs
index 4d2168e31..687455740 100644
--- a/ml/src/risk/advanced_risk_engine.rs
+++ b/ml/src/risk/advanced_risk_engine.rs
@@ -18,8 +18,6 @@ use thiserror::Error;
 use tokio::sync::mpsc;
 
 use crate::{MLResult, MLError};
-// use crate::safe_operations; // DISABLED - module not found
-
 /// Monte Carlo simulation for VaR calculation
 pub fn simulate_random_shock(volatility: f64) -> f64 {
     let mut rng = rand::thread_rng();
diff --git a/ml/src/risk/circuit_breakers.rs b/ml/src/risk/circuit_breakers.rs
index c370717f2..62273c29b 100644
--- a/ml/src/risk/circuit_breakers.rs
+++ b/ml/src/risk/circuit_breakers.rs
@@ -357,7 +357,7 @@ impl MLCircuitBreaker {
 #[cfg(test)]
 mod tests {
     use super::*;
-    // use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_circuit_breaker_creation() -> Result<()> {
diff --git a/ml/src/risk/extreme_value_models.rs b/ml/src/risk/extreme_value_models.rs
index 836e40038..522b7f89b 100644
--- a/ml/src/risk/extreme_value_models.rs
+++ b/ml/src/risk/extreme_value_models.rs
@@ -25,7 +25,7 @@ use chrono::{DateTime, Utc};
 use serde::{Deserialize, Serialize};
 
 use super::*;
-// use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_neural_evt_model_creation() {
diff --git a/ml/src/risk/kelly_optimizer.rs b/ml/src/risk/kelly_optimizer.rs
index 864671c63..bf203dee3 100644
--- a/ml/src/risk/kelly_optimizer.rs
+++ b/ml/src/risk/kelly_optimizer.rs
@@ -210,7 +210,7 @@ impl KellyCriterionOptimizer {
 #[cfg(test)]
 mod tests {
     use super::*;
-    // use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_basic_kelly_calculation() -> Result<()> {
diff --git a/ml/src/risk/lstm_gan_scenarios.rs b/ml/src/risk/lstm_gan_scenarios.rs
index 940b330b3..d548f69a4 100644
--- a/ml/src/risk/lstm_gan_scenarios.rs
+++ b/ml/src/risk/lstm_gan_scenarios.rs
@@ -24,7 +24,7 @@ use super::*;
 #[cfg(test)]
 mod tests {
     use super::*;
-// use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_lstm_layer_creation() {
diff --git a/ml/src/risk/monitor.rs b/ml/src/risk/monitor.rs
index 60cd583b1..010989130 100644
--- a/ml/src/risk/monitor.rs
+++ b/ml/src/risk/monitor.rs
@@ -137,7 +137,7 @@ impl RealTimeMonitor {
 mod tests {
     use super::*;
     use tokio::test;
-// use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     async fn test_monitor_creation() {
diff --git a/ml/src/risk/position_sizing.rs b/ml/src/risk/position_sizing.rs
index 47118b21f..525951f71 100644
--- a/ml/src/risk/position_sizing.rs
+++ b/ml/src/risk/position_sizing.rs
@@ -7,9 +7,7 @@ use ndarray::Array1;
 
 use crate::MLResult as Result;
 
-// Import and re-export canonical types
-// pub use common::position_sizing::PositionSizingRecommendation; // Commented out - module not available
-// Using placeholder type
+// Using placeholder type for PositionSizingRecommendation
 #[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
 pub struct PositionSizingRecommendation {
     pub recommended_size: f64,
diff --git a/ml/src/risk/var_models.rs b/ml/src/risk/var_models.rs
index c13b7cdfe..774336f5a 100644
--- a/ml/src/risk/var_models.rs
+++ b/ml/src/risk/var_models.rs
@@ -308,7 +308,7 @@ impl FeatureScaler {
 #[cfg(test)]
 mod tests {
     use super::*;
-    // use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_neural_var_model_creation() -> Result<()> {
diff --git a/ml/src/tft/gated_residual.rs b/ml/src/tft/gated_residual.rs
index 747e3e9cf..0b9820c05 100644
--- a/ml/src/tft/gated_residual.rs
+++ b/ml/src/tft/gated_residual.rs
@@ -213,7 +213,7 @@ impl GRNStack {
 mod tests {
     use super::*;
     use candle_core::{DType, Device};
-    // use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_grn_creation() -> Result<(), MLError> {
diff --git a/ml/src/tft/hft_optimizations.rs b/ml/src/tft/hft_optimizations.rs
index 90fc11e87..1e7926b81 100644
--- a/ml/src/tft/hft_optimizations.rs
+++ b/ml/src/tft/hft_optimizations.rs
@@ -779,7 +779,7 @@ pub struct LatencyPercentiles {
 mod tests {
     use super::*;
     use candle_core::DType;
-    // use crate::safe_operations; // DISABLED - module not found
+
 
     //[test]
     fn test_memory_pool_allocation() {
diff --git a/ml/src/tft/quantile_outputs.rs b/ml/src/tft/quantile_outputs.rs
index f13a78df1..0b53beb79 100644
--- a/ml/src/tft/quantile_outputs.rs
+++ b/ml/src/tft/quantile_outputs.rs
@@ -252,7 +252,7 @@ impl QuantileLayer {
 mod tests {
     use super::*;
     use candle_core::{DType, Device};
-    // use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_quantile_layer_creation() -> Result<(), MLError> {
diff --git a/ml/src/tft/variable_selection.rs b/ml/src/tft/variable_selection.rs
index fe72d450f..54ca2be5d 100644
--- a/ml/src/tft/variable_selection.rs
+++ b/ml/src/tft/variable_selection.rs
@@ -198,7 +198,7 @@ impl VariableSelectionNetwork {
 mod tests {
     use super::*;
     use candle_core::DType;
-    // use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_variable_selection_network_creation() -> Result<(), MLError> {
diff --git a/ml/src/tlob/transformer.rs b/ml/src/tlob/transformer.rs
index a94b0ef06..b4fd252f6 100644
--- a/ml/src/tlob/transformer.rs
+++ b/ml/src/tlob/transformer.rs
@@ -316,7 +316,7 @@ impl TLOBTransformer {
 mod tests {
     use super::*;
     use std::thread;
-    // use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_tlob_transformer_creation() -> Result<(), MLError> {
diff --git a/ml/src/training/dqn_trainer.rs b/ml/src/training/dqn_trainer.rs
index 487745662..ae8c6d62e 100644
--- a/ml/src/training/dqn_trainer.rs
+++ b/ml/src/training/dqn_trainer.rs
@@ -7,7 +7,6 @@ use std::collections::{HashMap, VecDeque};
 use std::sync::Arc;
 
 use chrono::{DateTime, Utc};
-// use error_handling::{FoxhuntError, AppResult, ErrorSeverity}; // Commented out - crate doesn't exist
 use rand::prelude::*; // Replace common::rng with standard rand
 use ndarray::{Array1, Array2, Axis, s};
 use serde::{Serialize, Deserialize};
diff --git a/ml/src/training/transformer_trainer.rs b/ml/src/training/transformer_trainer.rs
index bc8865b6b..377e5e746 100644
--- a/ml/src/training/transformer_trainer.rs
+++ b/ml/src/training/transformer_trainer.rs
@@ -7,7 +7,6 @@ use std::collections::{HashMap, VecDeque};
 use std::sync::Arc;
 
 use chrono::{DateTime, Utc};
-// use error_handling::{FoxhuntError, AppResult, ErrorSeverity}; // Commented out - crate doesn't exist
 use rand::prelude::*; // Replace common::rng with standard rand
 use ndarray::{Array1, Array2, Array3, Axis, s};
 use serde::{Serialize, Deserialize};
diff --git a/ml/src/training/unified_data_loader.rs b/ml/src/training/unified_data_loader.rs
index 6b04965d3..d419e0f70 100644
--- a/ml/src/training/unified_data_loader.rs
+++ b/ml/src/training/unified_data_loader.rs
@@ -23,8 +23,7 @@ use tracing::{debug, info};
 use crate::safety::MLSafetyManager;
 use crate::{MLError, MLResult};
 use common::types::{Price, Symbol, Volume};
-// use adaptive_strategy::microstructure::OrderLevel; // Commented out - crate not available
-// Using placeholder OrderLevel type instead
+// Using placeholder OrderLevel type
 #[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
 pub struct OrderLevel {
     pub price: f64,
diff --git a/ml/src/training_pipeline.rs b/ml/src/training_pipeline.rs
index 32ee0113f..325a9433e 100644
--- a/ml/src/training_pipeline.rs
+++ b/ml/src/training_pipeline.rs
@@ -21,8 +21,6 @@ use tokio::sync::{Mutex, RwLock};
 use tracing::{error, info, warn};
 use uuid::Uuid;
 
-// use error_handling::{AppResult, TradingError}; // Commented out - crate doesn't exist
-
 use crate::safety::{
     GradientSafetyConfig, GradientSafetyManager, GradientStatistics, MLSafetyConfig,
     MLSafetyManager, SafetyResult,
diff --git a/ml/src/transformers/attention.rs b/ml/src/transformers/attention.rs
index eb7a5c156..768766b7a 100644
--- a/ml/src/transformers/attention.rs
+++ b/ml/src/transformers/attention.rs
@@ -5,7 +5,7 @@
 #[cfg(test)]
 mod tests {
     use crate::tft::temporal_attention::AttentionConfig;
-    // use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_attention_config() {
diff --git a/ml/src/transformers/features.rs b/ml/src/transformers/features.rs
index 54b1c9dae..20f670629 100644
--- a/ml/src/transformers/features.rs
+++ b/ml/src/transformers/features.rs
@@ -26,7 +26,7 @@ use chrono::{DateTime, Datelike, Timelike, Utc};
 use serde::{Deserialize, Serialize};
 
 use super::*;
-// use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_feature_config_default() {
diff --git a/ml/src/transformers/financial_transformer.rs b/ml/src/transformers/financial_transformer.rs
index 8379fe676..456b7faed 100644
--- a/ml/src/transformers/financial_transformer.rs
+++ b/ml/src/transformers/financial_transformer.rs
@@ -10,7 +10,7 @@ use serde::{Deserialize, Serialize};
 
 use crate::{MLAppResult, TrainingMetrics};
 use super::*;
-// use crate::safe_operations; // DISABLED - module not found
+
 
     #[tokio::test]
     async fn test_financial_transformer_creation() {
diff --git a/ml/src/transformers/hft_transformer.rs b/ml/src/transformers/hft_transformer.rs
index 6457692b4..d43dd15f9 100644
--- a/ml/src/transformers/hft_transformer.rs
+++ b/ml/src/transformers/hft_transformer.rs
@@ -21,13 +21,11 @@ use async_trait::async_trait;
 use candle_core::Device;
 use candle_core::{Device, Tensor, DType, Result as CandleResult, Module};
 use candle_nn::{Linear, LayerNorm, Activation, VarBuilder, VarMap};
-// use error_handling::{FoxhuntError, AppResult}; // Commented out - crate doesn't exist
 use serde::{Serialize, Deserialize};
 use tracing::{info, debug, warn, error};
 
 use crate::{traits::MLModel, ModelMetadata, InferenceResult, TrainingMetrics, ValidationMetrics};
 use super::*;
-// use crate::safe_operations; // DISABLED - module not found
 
     #[tokio::test]
     async fn test_hft_transformer_creation() {
diff --git a/ml/src/transformers/quantization.rs b/ml/src/transformers/quantization.rs
index b0a382024..ea32ab37d 100644
--- a/ml/src/transformers/quantization.rs
+++ b/ml/src/transformers/quantization.rs
@@ -5,14 +5,13 @@
 
 use candle_core::Device;
 use candle_core::{Device, Result as CandleResult, Tensor};
-// use error_handling::AppResult; // Commented out - crate doesn't exist
 use serde::{Deserialize, Serialize};
 use tracing::{info, warn};
 use tracing::{info, warn};
 use tracing::{info, warn};
 
 use super::*;
-// use crate::safe_operations; // DISABLED - module not found
+
 
     #[test]
     fn test_quantization_config() {