diff --git a/CHANGELOG.md b/CHANGELOG.md
index 8bd6aeb8..50802eef 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,18 +1,35 @@
 # Release Notes
 
-## v0.9.0 House keeping (UNRELEASED)
+## v0.9.0 Simplified API (04/06/2021)
 
 ### Breaking changes
 
 - Removed `dropout_categoricals` parameter from `TimeSeriesDataSet`.
   Use `categorical_encoders=dict(<variable_name>=NaNLabelEncoder(add_nan=True)`) instead (#518)
 - Rename parameter `allow_missings` for `TimeSeriesDataSet` to `allow_missing_timesteps` (#518)
+- Transparent handling of transformations. Forward methods should now call two new methods (#518):
+
+  - `transform_output` to explicitly rescale the network outputs into the de-normalized space
+  - `to_network_output` to create a dict-like named tuple. This allows tracing the modules with PyTorch's JIT. Only `prediction` is still required which is the main network output.
+
+  Example:
+
+  ```python
+  def forward(self, x):
+      normalized_prediction = self.module(x)
+      prediction = self.transform_output(prediction=normalized_prediction, target_scale=x["target_scale"])
+      return self.to_network_output(prediction=prediction)
+  ```
 
 ### Fixed
 
 - Fix quantile prediction for tensors on GPUs for distribution losses (#491)
 - Fix hyperparameter update for RecurrentNetwork.from_dataset method (#497)
 
+### Added
+
+- Improved validation of input parameters of TimeSeriesDataSet (#518)
+
 ## v0.8.5 Generic distribution loss(es) (27/04/2021)
 
 ### Added