Deploying ML Web App on Google Kubernetes Engine -Autopilot

By the end of this project, we will be having a hands-on practical experience of creating a Google cloud project, running dog breed classification Streamlit web app locally, creating a docker image of our machine learning web app and saving it in Google Container Registry (GCR), creating a GKE-Autopilot cluster, creating a Kubernetes deployment and service, testing the web app running on GKE-Autopilot and finally, deleting the project to avoid incurring charges to our Google Cloud account.