Update how-to-deploy-custom-container.md

Author: dem108

articles/machine-learning/how-to-deploy-custom-container.md

@@ -230,15 +230,15 @@ There are a few important concepts to note in this YAML/Python parameter:
 
 #### Base image
 
-The base image is specified as a parameter in environment, and `docker.io/tensorflow/serving:latest` is used in this example. As you inspect the container, you can find that this server uses `ENTRYPOINT` to start an entry point script, which takes the environment variables such as `MODEL_BASE_PATH` and `MODEL_NAME`, and exposed ports like `8501`. These details are all specific information for this chosen server. You can use this understanding of the server, to determine how to define the deployment. For example, if you set environment variables for `MODEL_BASE_PATH` and `MODEL_NAME` in the deployment definition, the server (in this case, TF Serving) will take the values to initiate the server. Likewise, if you set the port for the routes to be `8501` in the deployment definition, the user request to such routes will be correctly routed to the TF Serving server.
+The base image is specified as a parameter in environment, and `docker.io/tensorflow/serving:latest` is used in this example. As you inspect the container, you can find that this server uses `ENTRYPOINT` to start an entry point script, which takes the environment variables such as `MODEL_BASE_PATH` and `MODEL_NAME`, and exposes ports such as `8501`. These details are all specific information for this chosen server. You can use this understanding of the server, to determine how to define the deployment. For example, if you set environment variables for `MODEL_BASE_PATH` and `MODEL_NAME` in the deployment definition, the server (in this case, TF Serving) will take the values to initiate the server. Likewise, if you set the port for the routes to be `8501` in the deployment definition, the user request to such routes will be correctly routed to the TF Serving server.
 
-Note that this specific example is based on the TF Serving case, but you can use any containers that will stay up and respond to requests coming to liveness, readiness, and scoring routes.
+Note that this specific example is based on the TF Serving case, but you can use any containers that will stay up and respond to requests coming to liveness, readiness, and scoring routes. You can refer to other examples and see how the dockerfile is formed (for example, using `CMD` instead of `ENTRYPOINT`), 
 
 #### Inference config
 
 Inference config is a parameter in environment, and it specifies the port and path for 3 types of the route: liveness, readiness, and scoring route. Inference config is required if you want to run your own container with managed online endpoint.
 
-#### Readiness route vs. liveness route
+#### Readiness route vs liveness route
 
 The API server you choose may provide a way to check the status of the server. There are two types of the route that you can specify: _liveness_ and _readiness_. A liveness route is used to check whether the server is running. A readiness route is used to check whether the server is ready to do work. In the context of machine learning inferencing, a server could respond 200 OK to a liveness request before loading a model, and the server could respond 200 OK to a readiness request only after the model is loaded into the memory.