MLOps at Scale: Building Enterprise-Grade Model Pipelines with Kubeflow and Vertex AI

The State of MLOps in 2024

According to McKinsey, enterprises scaling AI report 3-5x faster model deployment cycles. This guide analyzes architectures from leading AI companies deploying 50+ models in production.

Feature Store Architecture

Netflix’s Feast implementation handles 15TB of daily feature updates:

# Feature store provisioning (Terraform)
resource "google_bigquery_dataset" "feature_store" {
  dataset_id = "prod_feature_store"
  location   = "US"
  labels = {
    ml_team = "recommendations"
  }
}

resource "feast_feature_table" "user_profiles" {
  name = "user_embeddings"
  entities = ["user_id"]
  features = [
    {
      name = "last_30d_engagement"
      dtype = "FLOAT"
    }
  ]
}

Model Training Pipelines

Kubeflow Pipelines DSL for GPU-optimized training:

@component(
    packages_to_install=["tensorflow==2.12.0"]
)
def train_component(
    data_path: InputPath(),
    model_path: OutputPath(),
    epochs: int = 50
):
    import tensorflow as tf
    
    strategy = tf.distribute.MirroredStrategy()
    with strategy.scope():
        model = build_keras_model()
        
    train_data = load_dataset(data_path)
    model.fit(train_data, epochs=epochs)
    model.save(model_path)

Production Monitoring

Alerts for data drift using KS-test and PSI metrics:

CI/CD for ML Models

GitOps workflow for model deployments:

apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
  name: model-deploy
spec:
  destination:
    namespace: ml-production
    server: https://kubernetes.default.svc
  source:
    repoURL: git@github.com:company/ml-models.git
    path: kustomize/prod
    targetRevision: HEAD
  syncPolicy:
    automated:
      prune: true
      selfHeal: true

Case Study: Financial Fraud Detection

JPMorgan Chase’s pipeline improvements:

• Feature lookup latency reduced from 120ms to 8ms using Redis
• Automated retraining reduced false positives by 22%
• Model version rollbacks within 90 seconds