Merge pull request #18 from caetano-colin/docs/add-model-validation-step

docs: add model validation step

6-ml-pipeline/dev/Readme.md

@@ -107,8 +107,54 @@ The compile_pipeline.py and runpipeline.py files are commented to point out thes
 ### 4. Merge and deploy
 - Once everything is configured, you can commit your changes and push to the dev branch. Then, create a PR to from dev to staging(non-prod) which will result in triggering the pipeline if approved. The vertex pipeline takes about 30 minutes to finish and if there are no errors, a trained model will be deployed to and endpoint in the prod project which you can use to make prediction requests.
 
+### 5. Model Validation
 
+Once you have the model running at an endpoint in the production project, you will be able to test it.
+Here are step-by-step instructions to make a request to your model using `gcloud` and `curl`:
 
+1. Initialize variables on your terminal session
+
+    ```bash
+    ENDPOINT_ID=<REPLACE_WITH_ENDPOINT_ID>
+    PROJECT_ID=<REPLACE_WITH_PROJECT_ID>
+    INPUT_DATA_FILE="body.json"
+    ```
+
+    > You can retrieve your ENDPOINT_ID by running `gcloud ai endpoints list --region=us-central1 --project=<PROD_ML_PROJECT>` or by navigating to it on the Google Cloud Console (https://console.cloud.google.com/vertex-ai/online-prediction/endpoints?project=<PROD_ML_PROJECT>`)
+
+2. Create a file named `body.json` and put some sample data into it:
+
+    ```json
+    {
+        "instances": [
+            {
+                "features/gender": "Female",
+                "features/workclass": "Private",
+                "features/occupation": "Tech-support",
+                "features/marital_status": "Married-civ-spouse",
+                "features/race": "White",
+                "features/capital_gain": 0,
+                "features/education": "9th",
+                "features/age": 33,
+                "features/hours_per_week": 40,
+                "features/relationship": "Wife",
+                "features/native_country": "Canada",
+                "features/capital_loss": 0
+            }
+        ]
+    }
+    ```
+
+3. Run a curl request using `body.json` file as the JSON Body.
+
+    ```bash
+    curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
+    -H "Content-Type: application/json" \
+    https://us-central1-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/us-central1/endpoints/${ENDPOINT_ID}:predict -d "@${INPUT_DATA_FILE}"
+    ```
+
+    - You should get an output from 0 to 1, indicating the level of confidence of the binary classification based on the parameters above.
+    Values closer to 1 means the individual is more likely to be included in the income_bracket greater than 50K.
 
 # Common errors
 - ***google.api_core.exceptions.ResourceExhausted: 429 The following quotas are exceeded: ```CustomModelServingCPUsPerProjectPerRegion 8: The following quotas are exceeded: CustomModelServingCPUsPerProjectPerRegion``` or similar error***: