corrected typo (#1029)

docs/getting_started/hello_cifar.rst

@@ -28,7 +28,7 @@ In this example, we have three main pipelines: :code:`download-normalize`, :code
 
 First, we will have to retrieve and prepare the data by running the :code:`download-normalize` pipeline. This can be done by opening the given pipeline then selecting the `Execute Pipeline` option from the action button in the lower right. As soon as that pipeline finishes, we can now use this data to train a neural network.
 
-Next, we can open the :code:`train` pipeline. Before we execute the pipeline we have to set the input trainning data that we will be using. This is done by selecting the :code:`Input` operation then clicking the value for the :code:`artifact` field. This will provide all the possible options for the input data; for this example, we will want to select the "trainingdata" artifact. After setting the input, we can click on the :code:`train` operation to inspect the hyperparameters we are using and the architecture we are training. Selecting the :code:`Output` operation will allow you to change the name of the resulting artifact of this operation (in this case, a trained model). Finally, we can execute this pipeline like before to train the model.
+Next, we can open the :code:`train` pipeline. Before we execute the pipeline we have to set the input training data that we will be using. This is done by selecting the :code:`Input` operation then clicking the value for the :code:`artifact` field. This will provide all the possible options for the input data; for this example, we will want to select the "trainingdata" artifact. After setting the input, we can click on the :code:`train` operation to inspect the hyperparameters we are using and the architecture we are training. Selecting the :code:`Output` operation will allow you to change the name of the resulting artifact of this operation (in this case, a trained model). Finally, we can execute this pipeline like before to train the model.
 
 .. figure:: select_train_data.png
     :align: center