Chapter 05

Scenario 2: A cross-functional team with one data scientist working on an ML model

NotebookPython 3.9.12 ('exp-tracking-env')11 cells

Scenario 2: A cross-functional team with one data scientist working on an ML model

MLflow setup:

tracking server: yes, local server
backend store: sqlite database
artifacts store: local filesystem

The experiments can be explored locally by accessing the local tracking server.

To run this example you need to launch the mlflow server locally by running the following command in your terminal:

mlflow server --backend-store-uri sqlite:///backend.db

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python

import mlflow


mlflow.set_tracking_uri("http://127.0.0.1:5000")

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python

print(f"tracking URI: '{mlflow.get_tracking_uri()}'")

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python

mlflow.search_experiments()

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python

from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score

mlflow.set_experiment("my-experiment-1")

with mlflow.start_run():

    X, y = load_iris(return_X_y=True)

    params = {"C": 0.1, "random_state": 42}
    mlflow.log_params(params)

    lr = LogisticRegression(**params).fit(X, y)
    y_pred = lr.predict(X)
    mlflow.log_metric("accuracy", accuracy_score(y, y_pred))

    mlflow.sklearn.log_model(lr, artifact_path="models")
    print(f"default artifacts URI: '{mlflow.get_artifact_uri()}'")

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python

mlflow.search_experiments()

Interacting with the model registry

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python

from mlflow.tracking import MlflowClient


client = MlflowClient("http://127.0.0.1:5000")

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python

client.search_registered_models()

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python

run_id = client.search_runs(experiment_ids='1')[0].info.run_id
mlflow.register_model(
    model_uri=f"runs:/{run_id}/models",
    name='iris-classifier'
)

In [ ]python · cell 11

python