mlops

name	mlops
description	MLflow, model versioning, experiment tracking, model registry, and production ML systems
sasmp_version	1.3.0
bonded_agent	06-ml-ai-engineer
bond_type	PRIMARY_BOND
skill_version	2.0.0
last_updated	2025-01
complexity	advanced
estimated_mastery_hours	150
prerequisites	["machine-learning","containerization","python-programming"]
unlocks	["llms-generative-ai"]

MLOps

Production machine learning systems with MLflow, model versioning, and deployment pipelines.

Quick Start

import mlflow
from mlflow.tracking import MlflowClient
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, f1_score
import joblib

# Configure MLflow
mlflow.set_tracking_uri("http://mlflow-server:5000")
mlflow.set_experiment("customer-churn-prediction")

# Training with experiment tracking
with mlflow.start_run(run_name="rf-baseline"):
    # Log parameters
    params = {"n_estimators": 100, "max_depth": 10, "random_state": 42}
    mlflow.log_params(params)

    # Train model
    model = RandomForestClassifier(**params)
    model.fit(X_train, y_train)

    # Evaluate and log metrics
    y_pred = model.predict(X_test)
    metrics = {
        "accuracy": accuracy_score(y_test, y_pred),
        "f1_score": f1_score(y_test, y_pred, average="weighted")
    }
    mlflow.log_metrics(metrics)

    # Log model to registry
    mlflow.sklearn.log_model(
        model, "model",
        registered_model_name="churn-classifier",
        signature=mlflow.models.infer_signature(X_train, y_pred)
    )

    print(f"Run ID: {mlflow.active_run().info.run_id}")

Core Concepts

1. Model Registry & Versioning

from mlflow.tracking import MlflowClient

client = MlflowClient()

# Promote model to production
client.transition_model_version_stage(
    name=,
    version=,
    stage=
)


client.transition_model_version_stage(
    name=,
    version=,
    stage=
)


model_uri = 
model = mlflow.sklearn.load_model(model_uri)


 () -> :
    results = {}
     version  versions:
        run_id = client.get_model_version(model_name, (version)).run_id
        run = client.get_run(run_id)
        results[version] = run.data.metrics
     results