# 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 a DevOps/SRE/DevSecOps/Cloud Expert passionate about sharing knowledge and experiences. I have worked at Cotocus. I share tech blog at DevOps School, travel stories at Holiday Landmark, stock market tips at Stocks Mantra, health and fitness guidance at My Medic Plus, product reviews at TrueReviewNow , and SEO strategies at Wizbrand.
Do you want to learn Quantum Computing?
Please find my social handles as below;
Rajesh Kumar Personal Website
Rajesh Kumar at YOUTUBE
Rajesh Kumar at INSTAGRAM
Rajesh Kumar at X
Rajesh Kumar at FACEBOOK
Rajesh Kumar at LINKEDIN
Rajesh Kumar at WIZBRAND
Find Trusted Cardiac Hospitals
Compare heart hospitals by city and services — all in one place.
Explore Hospitals
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.