Merge pull request #5990 from newrelic/austin-schaefer-patch-3

style(MLOps): Use proper code blocks

src/content/docs/mlops/bring-your-own/mlops-byo.mdx

@@ -20,11 +20,11 @@ Use the python package to send the following types of data to New Relic:
 
 1. **Inference data:** Stream your model’s feature and prediction values. Choose between: 
 
-  * **“Online” instrumentation:** Stream the values while the model is being invoked in production by easily wrapping your model through the `wrap_model` function. The data is streamed automatically on each invocation.
+    * **“Online” instrumentation:** Stream the values while the model is being invoked in production by easily wrapping your model through the `wrap_model` function. The data is streamed automatically on each invocation.
 
-  * **"Offline" instrumentation:** Send the data (features and predictions) as a dataset (as an `np.array`, or pandas dataframe).
+    * **"Offline" instrumentation:** Send the data (features and predictions) as a dataset (as an `np.array`, or pandas dataframe).
 
-  Inference data is streamed as a **[custom event](/docs/data-apis/ingest-apis/introduction-event-api/)** named **"InferenceData"**.
+    Inference data is streamed as a **[custom event](/docs/data-apis/ingest-apis/introduction-event-api/)** named `InferenceData`.
 
 2. **Data metrics:** Instead of sending all your raw inference data, select the aggregated statistics option over the features and predictions (for example, `min`, `max`, `average`, or `percentile`). They will be sent automatically as **[metrics](/docs/data-apis/ingest-apis/metric-api/introduction-metric-api/)**. 
 
@@ -36,24 +36,29 @@ Follow these steps to stream your data and view it on the New Relic platform:
 
 1. **Get your license key:** Use the [license key](/docs/apis/intro-apis/new-relic-api-keys/#ingest-license-key) for the New Relic account you want your data to be associated with. 
 
-2. **Stream your data to New Relic** by using the [new-relic-ml-performance-monitoring](https://github.com/newrelic-experimental/ml-performance-monitoring) package. Follow the example below to see how to send data from your code.
+2. **Stream your data to New Relic** with the [new-relic-ml-performance-monitoring](https://github.com/newrelic-experimental/ml-performance-monitoring) package. Follow the example below to see how to send data from your code.
 
 3. **View your data in the New Relic Platform:**
 
-  * **Query your data:** Use the [data explorer](/docs/query-your-data/explore-query-data/browse-data/introduction-data-explorer/) to view the metrics and events data you sent, or use the following queries on the [query builder](/docs/query-your-data/explore-query-data/query-builder/introduction-query-builder/):
-
-    `SELECT * FROM InferenceData WHERE model_name=[model_name] SINCE 1 day ago`
-    `SELECT * FROM Metric WHERE model_name=[model_name] SINCE 1 day ago`
-
-  * **Create your own dashboard:** Build your own [dashboard](/docs/query-your-data/explore-query-data/dashboards/introduction-dashboards/) to visualize the metrics you sent, or view the distributions of your features and predictions. See an example dashboard below.
-
-  * **Explore entities:** When you stream data to New Relic, an entity of the type `machine learning model` is automatically created, one per each model name. You can explore your model entities by selecting **Explorer** on [New Relic One](https://one.newrelic.com), and going to the Machine Learning section on the left navigation menu. 
+    * **Query your data:** Use the [data explorer](/docs/query-your-data/explore-query-data/browse-data/introduction-data-explorer/) to view the metrics and events data you sent, or use the following queries on the [query builder](/docs/query-your-data/explore-query-data/query-builder/introduction-query-builder/):
+
+      ```
+      SELECT * FROM InferenceData WHERE model_name=[model_name] SINCE 1 day ago
+      ```
+
+      ```
+      SELECT * FROM Metric WHERE model_name=[model_name] SINCE 1 day ago
+      ```
+
+    * **Create your own dashboard:** Build your own [dashboard](/docs/query-your-data/explore-query-data/dashboards/introduction-dashboards/) to visualize the metrics you sent, or view the distributions of your features and predictions. See an example dashboard below.
+
+    * **Explore entities:** When you stream data to New Relic, an entity of the type `machine learning model` is automatically created, one per each model name. You can explore your model entities by selecting **Explorer** on [New Relic One](https://one.newrelic.com), and going to the Machine Learning section on the left navigation menu. 
 
 ## Need an example for reference? Check out the following use case! [#use-case]
 
 Run the following example to stream data and view an example dashboard on your New Relic account:
 
-1. **Run the [example notebook](https://github.com/newrelic-experimental/ml-performance-monitoring/blob/main/examples/sklearn.RandomForestClassifier_on_Iris_dataset.ipynb):** You'll have to define your [ingest key](#stream-data) as an environment variable, or send it as a parameter. (Here's an [example notebook].](https://github.com/newrelic-experimental/ml-performance-monitoring/tree/main/examples))
+1. **Run the [example notebook](https://github.com/newrelic-experimental/ml-performance-monitoring/blob/main/examples/sklearn.RandomForestClassifier_on_Iris_dataset.ipynb):** You'll have to define your [ingest key](#stream-data) as an environment variable, or send it as a parameter. (Here's an [example notebook](https://github.com/newrelic-experimental/ml-performance-monitoring/tree/main/examples).)
 
-2. **View the data in the example dashboard:** [Import](docs/query-your-data/explore-query-data/dashboards/introduction-dashboards/#dashboards-import) the example [dashboard template JSON](https://github.com/newrelic-experimental/ml-performance-monitoring/blob/main/examples/RandomForestClassifier_example_dashboard.json), and update **YOUR_ACCOUNT_ID** with your account ID.
+2. **View the data in the example dashboard:** [Import](docs/query-your-data/explore-query-data/dashboards/introduction-dashboards/#dashboards-import) the example [dashboard template JSON](https://github.com/newrelic-experimental/ml-performance-monitoring/blob/main/examples/RandomForestClassifier_example_dashboard.json), and update `YOUR_ACCOUNT_ID` with your account ID.