Hi @NicolaCompton 

This persistent "Resource temporarily unavailable" error in Databricks with
Unity Catalog is often related to workspace permissions or configuration issues.
Let's try several troubleshooting approaches:

Solution 1: Workspace URL Format
Make sure you're using the correct workspace URL format:

Solution 2: Check Unity Catalog Permissions

Solution 3: Minimal Test Setup
Try this minimal version to isolate the issue:

import mlflow
import mlflow.sklearn
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris

# Reset MLflow settings
mlflow.end_run() # End any existing runs

# Minimal configuration
mlflow.set_tracking_uri("databricks")

# Simple model training and registration
X, y = load_iris(return_X_y=True)
clf = RandomForestClassifier(n_estimators=10, max_depth=3)

with mlflow.start_run() as run:
clf.fit(X, y)
mlflow.sklearn.log_model(clf, "iris_model")
run_id = run.info.run_id
print(f"Run ID: {run_id}")

# Wait a moment for the run to finalize
import time
time.sleep(5)

# Try registration with error handling
try:
model_uri = f"runs:/{run_id}/iris_model"
result = mlflow.register_model(
model_uri=model_uri,
name="principal_analysts.nco.iris_simple_test"
)
print(f"Success! Model version: {result.version}")
except Exception as e:
print(f"Registration failed: {type(e).__name__}: {e}")

The "Resource temporarily unavailable" error often indicates either:
1. Insufficient permissions for Unity Catalog model registration
2. Workspace configuration issues
3. Temporary connectivity issues

Try Solution 3 first to isolate the problem, then work backwards to your full implementation.