Small edits (#174)

docs/getting_started/ds/ds_second_steps.md

@@ -99,7 +99,11 @@ we need to pass a storage location for the model files to be uploaded to.
 
 For example, upload all snapshots to an S3 bucket:
 ```python
-task = Task.init(project_name='examples', task_name='storing model', output_uri='s3://my_models/')
+task = Task.init(
+    project_name='examples', 
+    task_name='storing model', 
+    output_uri='s3://my_models/'
+)
 ```
 
 Now, whenever the framework (TF/Keras/PyTorch etc.) stores a snapshot, the model file is automatically uploaded to the bucket to a specific folder for the experiment.

docs/getting_started/mlops/mlops_second_steps.md

@@ -43,7 +43,7 @@ dataset_folder = dataset.get_mutable_local_copy(
     overwrite=True
 )
 # change some files in the `./work_dataset` folder
-...
+
 # create a new version of the dataset with the pickle file
 new_dataset = Dataset.create(
     dataset_project='data', dataset_name='dataset_v2',