diff --git a/mlforecast/distributed/forecast.py b/mlforecast/distributed/forecast.py
index 401a1cde..defef0ad 100644
--- a/mlforecast/distributed/forecast.py
+++ b/mlforecast/distributed/forecast.py
@@ -377,7 +377,9 @@ def _fit(
         ]
         self.models_ = {}
         if SPARK_INSTALLED and isinstance(data, SparkDataFrame):
-            featurizer = VectorAssembler(inputCols=features, outputCol="features")
+            featurizer = VectorAssembler(
+                inputCols=features, outputCol="features", handleInvalid="keep"
+            )
             train_data = featurizer.transform(prep)[target_col, "features"]
             for name, model in self.models.items():
                 trained_model = model._pre_fit(target_col).fit(train_data)
diff --git a/nbs/distributed.forecast.ipynb b/nbs/distributed.forecast.ipynb
index 1ccd9418..e287d034 100644
--- a/nbs/distributed.forecast.ipynb
+++ b/nbs/distributed.forecast.ipynb
@@ -431,7 +431,9 @@
     "        features = [x for x in fa.get_column_names(prep) if x not in {id_col, time_col, target_col}]\n",
     "        self.models_ = {}\n",
     "        if SPARK_INSTALLED and isinstance(data, SparkDataFrame):\n",
-    "            featurizer = VectorAssembler(inputCols=features, outputCol=\"features\")\n",
+    "            featurizer = VectorAssembler(\n",
+    "                inputCols=features, outputCol=\"features\", handleInvalid=\"keep\"\n",
+    "            )\n",
     "            train_data = featurizer.transform(prep)[target_col, \"features\"]\n",
     "            for name, model in self.models.items():\n",
     "                trained_model = model._pre_fit(target_col).fit(train_data)\n",