I exported my home MySQL database of collectibles to a csv file using:

mysql -u root -p -e "SELECT * FROM collectibles.inventory;" | sed 's/\t/","/g;s/^/"/;s/$/"/' > collectibles.csv

That gave me a file that looked like this:

"id","character","figure_name","property","type","manufacturer","location","quantity","list_price","total_cost","approximate_value"
"1","Aang","Nendroid with Momo","Avatar the Last Airbender","Action Figure","Good Smile Co","Collectible Room","1","40.00","40.00","53.20"
"2","Alice","D-Stage Alice","Alice In Wonderland","PVC Figure","Beast Kingdom","Art Room","1","20.00","20.00","26.60"
"3","Alice","Disney Animators Collection Alice Doll","Alice In Wonderland","Dolls","Disney","Art Room","1","29.99","29.99","39.89"
"4","Alice","Disney Store Classic","Alice In Wonderland","Dolls","Disney","Art Room","1","19.99","19.99","26.59"

Then I used that file for the rest of the task.

There were many iterations to get to the working code and I noticed that the more exact you are, even when troubleshooting, the better off your are. I would give it the error messages and it would forget the things we already had done and would suggest it again. By telling it to forget everthing that happened before, it would wipe it's memory and start over.

The first step should be to know the steps involved or have AI tell you the steps before you ask it to do things. Had I started with asking what steps are required, I could have been more specific in my requests and probably gotten done more quickly.

Sometimes it's best to tell it to forget everything so it doesn't get in a loop.

Notebooks is a IDE that injects code into a compute instance or cluster and everything is additive until you restart the kernel. By this I mean if you define a script/variable that is saved to the compute instance, you can still access it from other scripts as you are “installing” things on the compute. When you restart the temporary memory is wiped and you have a fresh environment.

Upload your local 'collectibles.csv' to the Azure ML Notebooks file system using the file upload icon.

from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential

ml_client = MLClient(
  DefaultAzureCredential(),
  subscription_id="baa29726-b3e6-4910-bb9b-b585c655322c",
  resource_group_name="don-test-rg-SCUS",
  workspace_name="don-ml-workspace-fixed"
)

import pandas as pd
from sklearn.ensemble import RandomForestRegressor
import joblib

df = pd.read_csv("collectibles.csv")
df = df.dropna(subset=["approximate_value"])
X = df[["character", "location"]].astype(str)
y = df["approximate_value"]

from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline

preprocessor = ColumnTransformer([
    ("cat", OneHotEncoder(handle_unknown="ignore"), ["character", "location"])
])

model = Pipeline([
    ("preprocessor", preprocessor),
    ("regressor", RandomForestRegressor(n_estimators=10))
])

model.fit(X, y)
joblib.dump(model, "model.joblib")

# score.py
import joblib
import pandas as pd

def init():
    global model
    model = joblib.load("model.joblib")

def run(data):
    try:
        input_df = pd.DataFrame([{
            "character": data.get("character", ""),
            "location": data.get("location", "")
        }])
        prediction = model.predict(input_df)
        return {"prediction": prediction[0]}
    except Exception as e:
        return {"error": str(e)}

from azure.ai.ml.entities import Model

registered_model = ml_client.models.create_or_update(
    Model(
        path="model.joblib",
        name="collectibles-model",
        type="custom_model",
        description="Model predicting value based on character and location"
    )
)

from azure.ai.ml.entities import ManagedOnlineEndpoint, ManagedOnlineDeployment
import uuid

endpoint_name = f"collectibles-endpoint-{str(uuid.uuid4())[:8]}"

endpoint = ManagedOnlineEndpoint(
    name=endpoint_name,
    auth_mode="key"
)
ml_client.begin_create_or_update(endpoint).result()

deployment = ManagedOnlineDeployment(
    name="blue",
    endpoint_name=endpoint_name,
    model=registered_model.id,
    code_configuration={"code": ".", "scoring_script": "score.py"},
    instance_type="Standard_DS3_v2",
    instance_count=1
)
ml_client.begin_create_or_update(deployment).result()

ml_client.online_endpoints.begin_update(
    name=endpoint_name,
    update_parameters={"defaults": {"deployment_name": "blue"}}
).result()

import requests
import json

endpoint = ml_client.online_endpoints.get(name=endpoint_name)
scoring_uri = endpoint.scoring_uri
key = ml_client.online_endpoints.get_keys(name=endpoint_name).primary_key

input_data = {
    "location": "Gotham",
    "character": "Batman"
}

headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {key}"
}

response = requests.post(scoring_uri, headers=headers, data=json.dumps(input_data))
print("🔁 Status Code:", response.status_code)
print("🔁 Response:", response.text)

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