MLflow for Experiment Tracking: Setup, Usage, and When It Is Enough

MLflow tracks parameters, metrics, and artifacts for ML experiments. Here is how to set it up in 5 lines, integrate with training loops, and when to use it over W&B.

Mahmudul Haque Qudrati

CEO & ML Engineer

May 17, 2026

8 min read

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MLflow for Experiment Tracking: Setup, Usage, and When It Is Enough

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Integration with Training Loops

MLflow's autolog feature integrates automatically with common ML frameworks:

import mlflow
import mlflow.sklearn
from sklearn.ensemble import RandomForestClassifier

mlflow.sklearn.autolog()  # Automatically logs params, metrics, and model

with mlflow.start_run():
    clf = RandomForestClassifier(n_estimators=100)
    clf.fit(X_train, y_train)
    # mlflow automatically logged: n_estimators, max_depth, accuracy, etc.

Autolog support: scikit-learn, PyTorch, TensorFlow/Keras, XGBoost, LightGBM, Statsmodels, and more.

For LLM fine-tuning with Hugging Face Transformers:

import mlflow

mlflow.set_experiment("llm-fine-tuning")

with mlflow.start_run() as run:
    mlflow.log_params({
        "model_name": "meta-llama/Meta-Llama-3-8B-Instruct",
        "lora_rank": 16,
        "learning_rate": 2e-4,
        "num_epochs": 3,
        "dataset_size": len(train_dataset)
    })

    # Training loop...
    for epoch, metrics in enumerate(training_metrics):
        mlflow.log_metrics({
            "train_loss": metrics["loss"],
            "eval_perplexity": metrics["perplexity"]
        }, step=epoch)

    # Log the LoRA adapter as an artifact
    mlflow.log_artifacts("./outputs/lora-adapter")

The Model Registry

MLflow's Model Registry versions production models and tracks their lifecycle (Staging, Production, Archived).

Registering a model:

with mlflow.start_run() as run:
    # ... training ...
    mlflow.sklearn.log_model(
        clf,
        "model",
        registered_model_name="customer-churn-classifier"
    )

Transitioning to production:

from mlflow import MlflowClient

client = MlflowClient()
client.transition_model_version_stage(
    name="customer-churn-classifier",
    version=3,
    stage="Production"
)

Loading the production model:

model = mlflow.sklearn.load_model("models:/customer-churn-classifier/Production")

The registry provides: version history, stage management, annotations, and a registry UI.

MLflow vs Weights & Biases

Feature	MLflow	W&B
Cost	Free (self-hosted)	$0/month (personal), $150+/month (team)
Setup	Requires infrastructure	Managed, works instantly
UI quality	Functional	Polished, better visualizations
Integrations	Good	Better (more native integrations)
Collaboration	Works on shared server	Built-in
Sweeps (HP tuning)	Basic	Excellent
Data viz	Basic	Rich

When MLflow is enough:

Small teams (1-5 people) comfortable managing a server
Cost-sensitive projects
Workloads where basic logging and model registry cover your needs
Teams that want to keep training data and model artifacts on their own infrastructure

When to choose W&B:

You want zero infrastructure management
Your team needs collaborative experiment comparison across many runs
You run hyperparameter sweeps and want W&B's sweep visualization
You value the polished UI and would pay for the time saved

Keep Reading

Fine-Tuning an LLM with QLoRA - MLflow tracking for LLM fine-tuning
Hugging Face Complete Guide - Hosting fine-tuned models after tracking experiments with MLflow
Open Source LLM Benchmarks 2026 - Evaluating models whose training you tracked with MLflow

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MLflow for Experiment Tracking: Setup, Usage, and When It Is Enough

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