📊 MLflow Integration

Track experiments, log metrics, and manage models with MLflow's ecosystem integrated directly into FlowyML pipelines.

📈 Experiment Tracking 🏷️ Model Registry 📊 Metric Logging

🧪 MLflow Integration

What you'll learn

How to auto-log metrics and models to MLflow — FlowyML + MLflow = automated experiment tracking with the industry-standard platform.

Track experiments, manage model versions, and deploy models using MLflow's open-source ecosystem.

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Why MLflow?

Feature	Benefit
Experiment Tracking	Log parameters, metrics, and artifacts
Model Registry	Version and manage model lifecycles
Universal	Works with any ML library
Open Source	No vendor lock-in

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🧪 Setup

pip install mlflow
mlflow server --host 127.0.0.1 --port 5000  # Start tracking server

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Auto-Logging

Enable MLflow tracking for your pipeline:

from flowyml.integrations.mlflow import MLflowTracker

pipeline.run(
    tracker=MLflowTracker(
        tracking_uri="http://localhost:5000",
        experiment_name="my_experiment",
    )
)

MLflowTracker Parameters

Parameter	Type	Default	Description
tracking_uri	str	required	MLflow tracking server URL
experiment_name	str	"default"	Experiment name
run_name	str	None	Custom run name
auto_log	bool	True	Auto-log step params/metrics

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📝 Custom Logging in Steps

Log custom metrics, parameters, and artifacts:

import mlflow
from flowyml import step

@step
def train_model(data):
    # Log parameters
    mlflow.log_param("lr", 0.01)
    mlflow.log_param("optimizer", "adam")

    model = train(data, lr=0.01)

    # Log metrics
    mlflow.log_metric("accuracy", 0.95)
    mlflow.log_metric("f1_score", 0.93)

    # Log the model artifact
    mlflow.sklearn.log_model(model, "model")

    return model

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📦 Model Registry

Register and promote models through lifecycle stages:

@step
def register_best_model(model, metrics):
    # Log and register in one step
    mlflow.sklearn.log_model(model, "model", registered_model_name="ProductionModel")

    # Promote to staging
    client = mlflow.tracking.MlflowClient()
    client.transition_model_version_stage(
        name="ProductionModel",
        version="1",
        stage="Staging",
    )

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Best Practices

Use autolog for quick wins

mlflow.autolog() automatically captures sklearn, XGBoost, LightGBM, and PyTorch metrics — zero code changes needed.

Remote tracking server

In production, point tracking_uri to a shared MLflow server so your whole team can see experiment results.

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🚀 What's Next?

📉 Weights & Biases

Visualize training runs with interactive dashboards and team collaboration.

Explore →

📈 Evaluations

Evaluate your models with built-in metrics, custom scorers, and comparison tools.

Learn more →

🔌 Plugin System

Extend FlowyML with custom plugins for logging, tracking, and notifications.

View Guide →