MLOps in Production: Building Automated Pipeline Infrastructure with Kubeflow & MLflow

Training a model on a Jupyter notebook is only 10% of the machine learning lifecycle. Building repeatable, automated MLOps pipelines that track data lineage, run automated retraining, and validate model performance requires robust infrastructure.

Here is a practical architectural blueprint for enterprise MLOps.

Core Pillars of an MLOps Engine

1. Feature Store: Centralized feature registry (e.g. Feast) ensuring zero training-serving skew.
2. Experiment Tracking: MLflow or Weights & Biases for logging hyperparameters, loss curves, and artifact binaries.
3. Pipeline Orchestration: Kubeflow Pipelines or Argo Workflows for multi-step DAG execution on Kubernetes.
4. Model Registry & Monitoring: Staging model artifacts and monitoring drift (Evidently AI) in production.

Sample Kubeflow Pipeline Definition

from kfp import dsl
from kfp.dsl import component

@component(base_image="python:3.10")
def train_model(data_path: str, model_output: dsl.Output[dsl.Model]):
    import pandas as pd
    # Training code execution...
    with open(model_output.path, "w") as f:
        f.write("model binary data")

@dsl.pipeline(name="mlops-training-pipeline")
def mlops_pipeline():
    train_task = train_model(data_path="s3://data-bucket/train.csv")

Monitoring Model & Concept Drift

Once a model is live in production, incoming request distributions inevitably drift over time. Setting up automated Prometheus metrics for output distribution shift ensures models are re-trained before performance degrades.