MLOps at Scale: Building Production-Ready ML Pipelines

Introduction

MLOps brings DevOps principles to machine learning, enabling organizations to deploy and maintain ML models at scale. This post explores the key components and best practices for building production-ready ML pipelines.

Core MLOps Components

1. Feature Store Implementation

from feast import FeatureStore, Entity, Feature, FeatureView
from feast.types import Float32, Int64

# Define an entity for our ML features
customer = Entity(
    name="customer_id",
    description="Customer identifier"
)

# Define feature view
customer_features = FeatureView(
    name="customer_features",
    entities=["customer_id"],
    ttl=timedelta(days=1),
    features=[
        Feature(name="total_purchases", dtype=Float32),
        Feature(name="account_age_days", dtype=Int64),
    ],
    online=True,
    input=customer_source,
)

2. Model Versioning and Registry

import mlflow

# Start MLflow run
with mlflow.start_run():
    # Log parameters
    mlflow.log_param("learning_rate", 0.01)
    mlflow.log_param("epochs", 100)

    # Train model
    model = train_model(params)

    # Log model
    mlflow.sklearn.log_model(
        model,
        "model",
        registered_model_name="customer_churn_predictor"
    )

Automated Pipeline Implementation

1. Training Pipeline

from kubeflow.pipelines import dsl

@dsl.pipeline(
    name='Training Pipeline',
    description='End-to-end training pipeline'
)
def training_pipeline(
    data_path: str,
    model_name: str,
    hyperparameters: dict
):
    # Data validation
    validate_op = dsl.ContainerOp(
        name='validate-data',
        image='data-validator:latest',
        arguments=['--data-path', data_path]
    )

    # Feature engineering
    feature_op = dsl.ContainerOp(
        name='feature-engineering',
        image='feature-engineer:latest',
        arguments=['--input-path', validate_op.output]
    )

    # Model training
    train_op = dsl.ContainerOp(
        name='model-training',
        image='model-trainer:latest',
        arguments=[
            '--features-path', feature_op.output,
            '--model-name', model_name,
            '--hyperparameters', hyperparameters
        ]
    )

2. Deployment Pipeline

from kubernetes import client
from kubernetes.client import V1Container

def deploy_model(model_uri: str, deployment_name: str):
    # Create deployment configuration
    container = V1Container(
        name="model-server",
        image="model-server:latest",
        ports=[client.V1ContainerPort(container_port=8080)],
        env=[
            client.V1EnvVar(
                name="MODEL_URI",
                value=model_uri
            )
        ]
    )

Conclusion

A strong MLOps foundation reduces deployment friction, improves reliability, and lets teams ship ML systems with confidence. Start small with the right abstractions, then scale with automation and monitoring.