diff --git a/training.py b/training.py
index 6b39e41..aa08a8c 100644
--- a/training.py
+++ b/training.py
@@ -154,32 +154,59 @@ print("Loading Kaggle ASL dataset...")
 base_path = 'asl_kaggle'
 train_df, sign_to_idx = load_kaggle_asl_data(base_path)
 
-# Process landmarks
-X = []
-y = []
+# Process landmarks with parallel processing
+from multiprocessing import Pool, cpu_count
+from functools import partial
 
-print("\nProcessing landmark files...")
-for idx, row in train_df.iterrows():
-    if idx % 1000 == 0:
-        print(f"Processed {idx}/{len(train_df)} sequences...")
 
+def process_single_sequence(row, base_path):
+    """Process a single sequence - designed for parallel execution"""
     parquet_path = os.path.join(base_path, row['path'])
 
     if not os.path.exists(parquet_path):
-        continue
+        return None, None
 
-    landmarks = extract_hand_landmarks_from_parquet(parquet_path)
+    try:
+        landmarks = extract_hand_landmarks_from_parquet(parquet_path)
 
-    if landmarks is None:
-        continue
+        if landmarks is None:
+            return None, None
 
-    features = get_optimized_features(landmarks)
+        features = get_optimized_features(landmarks)
 
-    if features is None:
-        continue
+        if features is None:
+            return None, None
 
-    X.append(features)
-    y.append(row['sign'])
+        return features, row['sign']
+    except Exception as e:
+        return None, None
+
+
+print("\nProcessing landmark files with parallel processing...")
+print(f"Using {cpu_count()} CPU cores")
+
+# Convert DataFrame rows to list for parallel processing
+rows_list = [row for _, row in train_df.iterrows()]
+
+# Create partial function with base_path
+process_func = partial(process_single_sequence, base_path=base_path)
+
+# Process in parallel with progress updates
+X = []
+y = []
+batch_size = 1000
+
+with Pool(processes=cpu_count()) as pool:
+    for i in range(0, len(rows_list), batch_size):
+        batch = rows_list[i:i + batch_size]
+        results = pool.map(process_func, batch)
+
+        for features, sign in results:
+            if features is not None and sign is not None:
+                X.append(features)
+                y.append(sign)
+
+        print(f"Processed {min(i + batch_size, len(rows_list))}/{len(rows_list)} sequences... (Valid: {len(X)})")
 
 print(f"\nSuccessfully processed {len(X)} sequences")