# Hello World ML on Databricks
import mlflow
import mlflow.sklearn
from mlflow.models import infer_signature
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
# 1. Load toy data
X, y = load_iris(return_X_y=True, as_frame=True)
# 2. Train a tiny model
model = LogisticRegression(max_iter=100).fit(X, y)
preds = model.predict(X)
# 3. Set experiment (will auto-create if not present)
mlflow.set_experiment("/Shared/hello_world_exp")
# 4. Log model with signature + input_example
with mlflow.start_run():
sig = infer_signature(X, preds)
mlflow.sklearn.log_model(
sk_model=model,
artifact_path="model",
signature=sig,
input_example=X.head(3),
registered_model_name="hello_world_model"
)
Code language: PHP (php)I’m Rajesh Kumar, a DevOps, SRE, DevSecOps, Cloud, and Platform Engineering expert passionate about sharing practical knowledge, real-world experiences, and industry best practices. I have worked at Cotocus and regularly write about technology, travel, investing, health, product reviews, and digital marketing through my various platforms.
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This short but powerful snippet is a great “Hello World” for anyone who wants to understand how Databricks, MLflow, and scikit-learn work together in a real ML workflow. In just a few lines, it shows how to load data, train a model, set an experiment, infer the model signature, and log everything properly into MLflow with a registered model name—exactly the pattern you’d reuse in real projects. For beginners and busy practitioners, this kind of minimal, working example is very useful to quickly grasp experiment tracking and model registry concepts before moving on to more complex pipelines.