Set up MLflow tracking on a real classification problem and you'll have something recruiters can actually click through — a live experiment server with 40+ logged runs, metric plots, and artifact registry — in under 30 minutes. Not a toy. Not a notebook screenshot. An actual MLflow UI showing your model evolution.
Most MLOps tutorials hand you a mlflow.autolog() one-liner and call it a day. That works until someone asks "why did run 17 outperform run 23?" and you have no logged hyperparameters to diff. The real value of MLflow isn't logging — it's structured comparison at query time.
How to Set Up MLflow Tracking in Under 10 Minutes
First, pick a dataset that has enough complexity to justify tracking. The CWRU bearing dataset or any sklearn toy dataset works, but I'll use the UCI Heart Disease dataset — 303 rows, 13 features, binary target. Small enough to iterate fast, real enough to show on a portfolio.
python
# requirements: mlflow==2.10.0, scikit-learn==1.4.0, pandas==2.1.4
import mlflow
import mlflow.sklearn
from mlflow.models.signature import infer_signature
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import (
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*Continue reading the full article on [TildAlice](https://tildalice.io/mlflow-experiment-tracking-portfolio-project/)*
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