deployment updates

Author: robzor92

docs/assets/images/guides/mlops/serving/deployment_adv_form_kserve.png

@@ -34,15 +34,25 @@ If you have at least one model already trained and saved in the Model Registry,
 
 Once in the deployments page, you can create a new deployment by either clicking on `New deployment` (if there are no existing deployments) or on `Create new deployment` it the top-right corner. Both options will open the deployment creation form.
 
-### Step 2: Choose a model server
+### Step 2: Choose a framework
 
-A simplified creation form will appear, including the most common deployment fields from all available configurations. These fields include the [model server](#model-server) and [custom script](#custom-script) (for python models).
+A simplified creation form will appear, including the most common deployment fields from all available configurations. The first step is to select your model to serve, which is done by first selecting the framework the model was registered as in the model registry. 
+
+For example if you registered the model as a TensorFlow model using `ModelRegistry.tensorflow.create_model(...)` you select `Tensorflow` in the dropdown.
+
+<p align="center">
+  <figure>
+    <img src="../../../../assets/images/guides/mlops/serving/deployment_simple_form_1.png" alt="Select the model framework">
+    <figcaption>Select the model framework</figcaption>
+  </figure>
+</p>
+
+All models registered for a specific framework will be listed in the model dropdown.
 
 <p align="center">
   <figure>
-    <img style="float: left; width: 45%; margin-right: 12px" src="../../../../assets/images/guides/mlops/serving/deployment_simple_form_tf.png" alt="Simplified deployment creation form for TensorFlow">
-    <img style="width: 45%;" src="../../../../assets/images/guides/mlops/serving/deployment_simple_form_py.png" alt="Deployment simplified creation form for Python">
-    <figcaption>Simplified deployment creation forms for TensorFlow models (left) and Python models (right)</figcaption>
+    <img style="max-width: 60%; margin: 0 auto" src="../../../../assets/images/guides/mlops/serving/deployment_simple_form_2.png" alt="Select the model">
+    <figcaption>Select the model</figcaption>
   </figure>
 </p>
 
@@ -59,13 +69,24 @@ For python models, if you want to use your own [predictor script](#step-2-option
   </figure>
 </p>
 
-### Step 4 (Optional): Enable KServe
+### Step 4 (Optional): Change predictor environment
+
+If you are using a predictor script  click on `From project` and navigate through the file system to find it, or click on `Upload new file` to upload a predictor script now.
+
+<p align="center">
+  <figure>
+    <img style="max-width: 80%; margin: 0 auto" src="../../../../assets/images/guides/mlops/serving/deployment_simple_form_py_pred.png" alt="Predictor script in the simplified deployment form">
+    <figcaption>Select a predictor script in the simplified deployment form</figcaption>
+  </figure>
+</p>
+
+### Step 5 (Optional): Enable KServe
 
 Other configuration such as the serving tool, is part of the advanced options of a deployment. To navigate to the advanced creation form, click on `Advanced options`.
 
 <p align="center">
   <figure>
-    <img style="max-width: 80%; margin: 0 auto" src="../../../../assets/images/guides/mlops/serving/deployment_simple_form_adv_options.png" alt="Advance options">
+    <img style="max-width: 60%; margin: 0 auto" src="../../../../assets/images/guides/mlops/serving/deployment_simple_form_adv_options.png" alt="Advance options">
     <figcaption>Advanced options. Go to advanced deployment creation form</figcaption>
   </figure>
 </p>
@@ -79,7 +100,7 @@ Here, you change the [serving tool](#serving-tool) for your deployment by enabli
   </figure>
 </p>
 
-### Step 5 (Optional): Other advanced options 
+### Step 6 (Optional): Other advanced options 
 
 Additionally, you can adjust the default values of the rest of components:
 
@@ -154,7 +175,7 @@ my_predictor = ms.create_predictor(my_model,
                                    )
 ```
 
-### Step 3: Create a deployment with the predictor
+### Step 5: Create a deployment with the predictor
 
 ```python
 

docs/assets/images/guides/mlops/serving/deployment_simple_form_1.png

@@ -49,7 +49,7 @@ By default, the dialog will create a Spark job. To instead configure a Jupyter N
   </figure>
 </p>
 
-### Step 4: Set the script
+### Step 4: Set the notebook
 
 Next step is to select the Jupyter Notebook to run. You can either select `From project`, if the file was previously uploaded to Hopsworks, or `Upload new file` which lets you select a file from your local filesystem as demonstrated below. By default, the job name is the same as the file name, but you can customize it as shown.