GCP- Dashboard Tutoring

I am looking for a tutor who can help me understand, in a comprehensive and step-by-step manner, how to deploy a production-ready dashboard that displays the results of a model.

My current stack includes:

Google Cloud Platform (GCP)

Vertex AI Workbench (where I trained the model)

Streamlit (for the dashboard)

Git and GitHub (for version control)

I already have:

A trained model in Workbench

The .py files developed

A structured and synchronized GitHub repository

The intention to deploy everything in GCP

However, I am not only looking for help with deployment — I want to fully understand the technical process behind it, including:

How to properly structure the project for production.

How to connect the trained model to the dashboard.

The best architecture choice in GCP for this use case (Cloud Run, App Engine, Vertex AI Endpoint, etc.).

How to automate the workflow using GitHub.

How to properly configure the environment (requirements, Docker if needed, environment variables).

How to manage credentials and permissions securely.

How to monitor the application once it is deployed.

Additionally, I want to understand how to optimize performance and reduce latency when users interact with the dashboard. Specifically, I am interested in:

Reducing inference time.

Avoiding reloading the model on every request.

Evaluating whether deploying the model to a Vertex AI Endpoint is a better approach.

Implementing caching or preloading strategies.

Best practices for running Streamlit applications in production.

My goal is not just to make it work, but to truly master the end-to-end process and be able to replicate it independently in future projects.