Fixed typos

06-cloud-deployment/README.md

@@ -2,7 +2,7 @@
 
 ## Overview
 
-In this lesson, we will deploy our weather data pipeline to Google Cloud Platform (GCP) using Google Kubernetes Engine (GKE). We'll cover the process of setting up a GKE cluster, building and pushing Docker images to Google Container Registry, and deploying our application components using Kubernetes.
+In this lesson, we will deploy our weather data pipeline to Google Cloud Platform (GCP) using Google Kubernetes Engine (GKE). We'll cover the process of setting up a GKE cluster, building and pushing Docker images to Google Artifact Registry, and deploying our application components using Kubernetes.
 
 By the end of this section, you will have:
 1. Set up a GKE cluster
@@ -153,7 +153,7 @@ This deployment process demonstrates how to take a locally developed data pipeli
 ## Key Points
 
 - GKE provides a managed Kubernetes environment, simplifying cluster setup and management
-- Building and pushing Docker images to Google Container Registry enables easy deployment to GKE
+- Building and pushing Docker images to Google Artifact Registry enables easy deployment to GKE
 - Kubernetes secrets provide a secure way to manage sensitive information like database credentials
 - Kubernetes jobs and cronjobs allow for scheduled and one-time execution of tasks
 - Monitoring and debugging tools in Kubernetes help manage and troubleshoot deployments
@@ -163,5 +163,5 @@ This deployment process demonstrates how to take a locally developed data pipeli
 - [Google Kubernetes Engine documentation](https://cloud.google.com/kubernetes-engine/docs)
 - [Kubernetes documentation](https://kubernetes.io/docs/home/)
 - [Docker documentation](https://docs.docker.com/)
-- [Google Container Registry documentation](https://cloud.google.com/container-registry/docs)
+- [Google Artifact Registry documentation](https://cloud.google.com/artifact-registry/docs)
 - [Kubernetes best practices](https://kubernetes.io/docs/concepts/configuration/overview/)