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You also need to make sure the foo.com development and operations teams have the right access and the right tools to build the application and deploy it. As developers write the code for the app, they can use Cloud Code within the IDE to push the code to Cloud Build, which then packages and tests…

For ML/AI projects, you can use the data in BigQuery to train models in Vertex AI. Your media, image, and other static file datasets from Cloud Storage can be directly imported into Vertex AI. You can create your own custom model or use the pretrained models. It’s a good idea to start with a pretrained…

Applications like foo.com generate real-time data (e.g., clickstream data) and batch data (e.g., logs). This data needs to be ingested, processed, and made ready for downstream systems in a data warehouse. From there it can be analyzed further by data analysts, data scientists, and ML engineers to gain insights and make predictions. You can ingest…

In certain situations, foo.com might need to send messages, notifications to the user, or events between various microservices. This is where an asynchronous messaging service such as Cloud Pub/Sub can be used to push notifications to a topic and have other services subscribe to the topic and take appropriate action on it asynchronously.

Let’s say foo.com is also available on mobile devices, which need images rendered in smaller mobile formats. You can decouple functionality like this from the web server and make it a function-as-a-service with Cloud Functions. This approach enables you to apply your image resizing logic to other applications as well. You can trigger the serverless…

Cloud Debugger allows you to inspect the state of running applications after deployment, without needing to stop or slow it down.

Cloud Trace provides visualization and analysis to understand request flow, service topology, and latency issues in your app.

Cloud Profiler continually analyzes your code’s performance on each service so that you can improve its speed and reduce your costs. And it is designed to run in production with effectively no performance impact.

Cloud Monitoring provides observability across your apps and infrastructure, regardless of where it is — Google Cloud, on-premises, or in other clouds. It supports a variety of metrics integrations and allows you to define custom metrics unique to your use cases and even send those metrics to external systems.

Cloud Logging collects log data from Google Cloud services automatically, and any custom log source, such as applications, on-premises sources, or other clouds, through the Ops Agent, open source fluentd, or an API.