diff --git a/docs/freqai-feature-engineering.md b/docs/freqai-feature-engineering.md
index 12e01e30d..415ac2c63 100644
--- a/docs/freqai-feature-engineering.md
+++ b/docs/freqai-feature-engineering.md
@@ -307,64 +307,7 @@ class MyCoolTransform(BaseTransform):
 
 If you have created your own custom `IFreqaiModel` with a custom `train()`/`predict()` function, *and* you still rely on `data_cleaning_train/predict()`, then you will need to migrate to the new pipeline. If your model does *not* rely on `data_cleaning_train/predict()`, then you do not need to worry about this migration.
 
-The conversion involves first removing `data_cleaning_train/predict()` and replacing them with a `define_data_pipeline()` and `define_label_pipeline()` function to your `IFreqaiModel` class:
-
-```python
-class MyCoolFreqaiModel(BaseRegressionModel):
-    def train(
-        self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
-    ) -> Any:
-
-        # ... your custom stuff
-
-        # Remove these lines
-        # data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
-        # self.data_cleaning_train(dk)
-        # data_dictionary = dk.normalize_data(data_dictionary)
-
-        # Add these lines. Now we control the pipeline fit/transform ourselves
-        dd = dk.make_train_test_datasets(features_filtered, labels_filtered)
-        dk.feature_pipeline = self.define_data_pipeline(threads=dk.thread_count)
-        dk.label_pipeline = self.define_label_pipeline(threads=dk.thread_count)
-
-        (dd["train_features"],
-         dd["train_labels"],
-         dd["train_weights"]) = dk.feature_pipeline.fit_transform(dd["train_features"],
-                                                                  dd["train_labels"],
-                                                                  dd["train_weights"])
-
-        (dd["test_features"],
-         dd["test_labels"],
-         dd["test_weights"]) = dk.feature_pipeline.transform(dd["test_features"],
-                                                             dd["test_labels"],
-                                                             dd["test_weights"])
-
-        dd["train_labels"], _, _ = dk.label_pipeline.fit_transform(dd["train_labels"])
-        dd["test_labels"], _, _ = dk.label_pipeline.transform(dd["test_labels"])
-
-    def predict(
-        self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
-    ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
-
-        # ... your custom stuff
-
-        # Remove these lines:
-        # self.data_cleaning_predict(dk)
-
-        # Add these lines:
-        dk.data_dictionary["prediction_features"], outliers, _ = dk.feature_pipeline.transform(
-            dk.data_dictionary["prediction_features"], outlier_check=True)
-
-        # Remove this line
-        # pred_df = dk.denormalize_labels_from_metadata(pred_df)
-
-        # Replace with these lines
-        pred_df, _, _ = dk.label_pipeline.inverse_transform(pred_df)
-        if self.freqai_info.get("DI_threshold", 0) > 0:
-            dk.DI_values = dk.feature_pipeline["di"].di_values
-        else:
-            dk.DI_values = np.zeros(len(outliers.index))
-        dk.do_predict = outliers.to_numpy()
+More details about the migration can be found [here](strategy_migration.md#freqai---new-data-pipeline).
 
 ## Outlier detection