MLOps in 2026: End-to-End ML Pipeline Architecture
Machine learning without MLOps is like software without DevOps — it works in the lab but fails in production. This guide covers the complete MLOps stack, from feature engineering through model deployment and monitoring.
The MLOps Stack
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Architecture Diagram
┌─────────────────────────────────────────────────────────┐
│ MLOps Platform │
├─────────┬──────────┬──────────┬──────────┬──────────────┤
│ Feature │ Model │ Pipeline │ Model │ Model │
│ Store │ Registry │ Orchestr.│ Serving │ Monitoring │
├─────────┼──────────┼──────────┼──────────┼──────────────┤
│ Feast │ MLflow │ Airflow │ Triton │ Evidently │
│ Tecton │ DVC │ Kubeflow │ BentoML │ Whylabs │
│ Hopsworks│ Weights│ Dagster │ Seldon │ Grafana │
└─────────┴──────────┴──────────┴──────────┴──────────────┘
Feature Store Architecture
Core Concepts
Feature Store Best Practices
Model Registry
MLflow Model Registry
Model Versioning Strategy
ML Pipeline Orchestration
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Airflow DAG for ML Pipeline
Dagster Pipeline
Model Monitoring
Data Drift Detection
Real-Time Monitoring Dashboard
CI/CD for ML
GitHub Actions Pipeline
# .github/workflows/ml-pipeline.yml
name: ML Pipeline
on:
push:
branches: [main]
schedule:
- cron: '0 0 * * 0' # Weekly
jobs:
train:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: '3.11'
- name: Install dependencies
run: pip install -r requirements.txt
- name: Run tests
run: pytest tests/
- name: Train model
run: python train.py
env:
MLFLOW_TRACKING_URI: ${{ secrets.MLFLOW_URI }}
- name: Evaluate model
run: python evaluate.py
- name: Deploy if approved
if: success()
run: python deploy.py
Model Testing
Production Architecture
Kubernetes Deployment
# model-serving.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: model-server
spec:
replicas: 3
selector:
matchLabels:
app: model-server
template:
metadata:
labels:
app: model-server
spec:
containers:
- name: model
image: model-server:latest
resources:
requests:
memory: "4Gi"
cpu: "2"
nvidia.com/gpu: "1"
limits:
memory: "8Gi"
cpu: "4"
nvidia.com/gpu: "1"
ports:
- containerPort: 8080
env:
- name: MODEL_PATH
value: "s3://models/production/model.pkl"
livenessProbe:
httpGet:
path: /health
port: 8080
initialDelaySeconds: 30
periodSeconds: 10
readinessProbe:
httpGet:
path: /ready
port: 8080
initialDelaySeconds: 5
periodSeconds: 5
---
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
name: model-server-hpa
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: model-server
minReplicas: 2
maxReplicas: 10
metrics:
- type: Resource
resource:
name: cpu
target:
type: Utilization
averageUtilization: 70
Cost Optimization
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Model Optimization Techniques
Conclusion
MLOps in 2026 requires a comprehensive approach covering the entire ML lifecycle:
- Feature Store — Centralized feature management with validation
- Model Registry — Version control and staged promotion
- Pipeline Orchestration — Automated training and deployment
- Monitoring — Real-time drift detection and alerting
- CI/CD — Automated testing and deployment
The key is to start simple and add complexity as needed. Begin with MLflow for tracking, Airflow for orchestration, and Evidently for monitoring. Scale up as your ML operations mature.