This guide securely deploys MLflow on Google Cloud Platform (GCP), leveraging Cloud Run, Cloud IAP, VPC egress, and GCS FUSE. It's designed for users comfortable with GCP and command-line interfaces.

Key Features:

• Secure MLflow Deployment: Utilizes Cloud Run for cost-effective, scalable MLflow backend server deployment. Cloud IAP and HTTPS load balancing restrict access to authorized users. MLflow artifacts are securely stored in Cloud Storage (GCS) via GCS FUSE, avoiding public internet exposure. Cloud SQL, accessed via VPC egress, manages metadata securely.
• Step-by-Step Instructions: The guide provides detailed steps, including prerequisite setup, IAM role creation, VPC network configuration, CloudSQL and GCS setup, secret management, artifact registry creation, domain preparation, Cloud Run deployment via the GUI, and IAP configuration.
• Programmatic IAP Access: The guide explains how to configure programmatic access to the MLflow server using OAuth 2.0 client credentials and service account keys.

Deployment Process Summary:

1. Prerequisites: Install gcloud CLI, direnv, create a GCP project and user, and clone the provided Git repository. Define environment variables (.envrc file).
2. API Enablement & IAM Roles: Enable necessary GCP APIs and create a custom IAM role with appropriate permissions for the MLflow service account.
3. VPC Network Creation: Create a VPC network and configure private service access using VPC peering for Cloud SQL.
4. CloudSQL Configuration: Create a Cloud SQL instance with a private IP address, a user, and a database.
5. GCS Setup: Create a GCS bucket with restricted access, only allowing access by the MLflow service account.
6. Secret Management: Store CloudSQL URI and GCS bucket path securely in Google Cloud Secrets.
7. Artifact Registry: Create a Docker repository and push the MLflow Docker image.
8. Domain & Load Balancing: Obtain a domain name, configure Cloud DNS, and deploy the Cloud Run service using the GUI, configuring the external load balancer and necessary settings (including network, secrets, and service account). Update DNS records.
9. Cloud IAP Configuration: Configure Cloud IAP to secure access to the MLflow server. Grant necessary IAM permissions (roles/iap.httpsResourceAccessor) to authorized users.
10. Programmatic Access: Configure programmatic access using OAuth client ID and service account key.

This detailed guide provides a robust and secure method for deploying MLflow on GCP. Remember to replace all placeholder values with your specific GCP project details. The guide also includes troubleshooting tips and FAQs.

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