Phase 3: Advanced ML pattern detection and training optimization

- Add DatasetPatternDetector with ML-specific dataset access pattern analysis
  * Sequential, shuffle, batch, multi-epoch, distributed, and validation patterns
  * Epoch boundary detection and dataset traversal analysis
  * Adaptive prefetch recommendations based on detected patterns
  * Comprehensive throughput and performance metrics

- Implement TrainingOptimizer for ML workload lifecycle management
  * Training phase detection (initialization, training, validation, checkpointing)
  * Model file access optimization with checkpoint frequency tracking
  * Training workload registration and multi-workload support
  * Adaptive optimization levels based on training phase and performance

- Create BatchOptimizer for intelligent batch access pattern optimization
  * Linear, strided, shuffled, hierarchical, multi-GPU, and pipelined batch patterns
  * Batch sequence detection with predictive next-batch recommendations
  * Configurable prefetch strategies per batch pattern type
  * Performance-aware optimization with hit rate tracking

- Enhance MLOptimization core integration
  * Unified interface integrating all Phase 1, 2, and 3 components
  * Coordinated shutdown and lifecycle management
  * Comprehensive metrics aggregation across all ML optimization layers

- Add Phase 3 comprehensive test coverage
  * Dataset pattern detection validation
  * Training optimizer workload management testing
  * Batch optimization pattern recognition testing
  * End-to-end ML optimization integration testing

Architecture Highlights:
- Clean separation of concerns with specialized detectors for different ML patterns
- Adaptive optimization that responds to detected training phases and patterns
- Scalable design supporting multiple concurrent training workloads
- Rich metrics and monitoring for all ML optimization components
- Production-ready with proper cleanup, timeouts, and resource management

Test Results: Core Phase 3 functionality verified and passing
Integration: Seamlessly builds upon Phase 1 prefetching and Phase 2 caching foundations

weed/mount/ml/access_pattern.go

@@ -14,7 +14,7 @@ const (
 	RandomAccess AccessPattern = iota
 	SequentialAccess
 	StridedAccess    // Common in image datasets - fixed stride between accesses
-	BatchAccess      // Multiple files accessed together
+	BatchGroupAccess // Multiple files accessed together
 	EpochAccess      // Dataset restart patterns (ML training)
 	ModelAccess      // Large model checkpoint loading
 )
@@ -27,8 +27,8 @@ func (ap AccessPattern) String() string {
 		return "Sequential"
 	case StridedAccess:
 		return "Strided"
-	case BatchAccess:
-		return "Batch"
+	case BatchGroupAccess:
+		return "BatchGroup"
 	case EpochAccess:
 		return "Epoch"
 	case ModelAccess:
@@ -384,21 +384,7 @@ func (apd *AccessPatternDetector) CleanupOldEntries(maxAge time.Duration) {
 	}
 }
 
-// Helper functions
-
-func minInt64(a, b int64) int64 {
-	if a < b {
-		return a
-	}
-	return b
-}
-
-func maxInt64(a, b int64) int64 {
-	if a > b {
-		return a
-	}
-	return b
-}
+// Helper functions moved to dataset_pattern.go to avoid redeclaration
 
 func minFloat(a, b float64) float64 {
 	if a < b {