Machine Learning Operations (MLOps): Best Practices for Production AI

What is MLOps?

MLOps (Machine Learning Operations) combines ML, DevOps, and data engineering to deploy and maintain ML systems in production reliably and efficiently.

Why MLOps Matters

The Production Gap

Most ML projects fail to reach production. MLOps bridges this gap.

Key Challenges

MLOps Lifecycle

Complete Pipeline

Data Pipeline:
├── Collection
├── Validation
├── Transformation
└── Feature Store

Training Pipeline:
├── Experiment tracking
├── Model training
├── Hyperparameter tuning
└── Model validation

Deployment Pipeline:
├── Model packaging
├── Testing
├── Deployment (canary/blue-green)
└── Serving infrastructure

Monitoring Pipeline:
├── Performance metrics
├── Data drift detection
├── Model drift detection
└── Alerting and retraining

Core Components

Experiment Tracking

What to Track

import mlflow

with mlflow.start_run():
    mlflow.log_param("learning_rate", 0.01)
    mlflow.log_param("epochs", 100)
    mlflow.log_metric("accuracy", 0.95)
    mlflow.log_metric("loss", 0.05)
    mlflow.log_artifact("model.pkl")
    mlflow.log_model(model, "model")

Feature Store

Purpose

• Feature reuse across models
• Point-in-time correctness
• Online/offline consistency
• Feature versioning
• Feature monitoring

Model Registry

Capabilities

• Model versioning
• Stage management (dev/staging/prod)
• Approval workflows
• Lineage tracking
• Deployment automation

Deployment Patterns

Serving Options

Deployment Strategies

Example: Canary Deployment

# Traffic splitting for canary
apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
spec:
  hosts:
  - model-service
  http:
  - route:
    - destination:
        host: model-v1
      weight: 90
    - destination:
        host: model-v2
      weight: 10  # Canary

Monitoring and Observability

What to Monitor

Drift Detection

from evidently import ColumnDriftReport

report = ColumnDriftReport()
report.run(
    reference_data=train_data,
    current_data=production_data
)
# Alerts if distribution shifts significantly

Monitoring Tools

Infrastructure

Platform Options

Open Source Stack

Complete OSS MLOps Stack:

Data: DVC + Great Expectations
Features: Feast
Training: Ray + MLflow
Serving: KServe / Seldon
Monitoring: Evidently + Prometheus/Grafana
Orchestration: Airflow / Kubeflow
Infrastructure: Kubernetes

LLMOps Extension

LLM-Specific Concerns

LLMOps Tools

Best Practices

Maturity Levels

Key Recommendations

Getting Started

Quick Start Path

Minimum Viable MLOps

Start With:
✅ Git for code
✅ MLflow for experiments
✅ Docker for packaging
✅ GitHub Actions for CI
✅ Basic monitoring (latency, errors)

"MLOps isn't about using every tool—it's about having the right processes to reliably operate ML systems. Start simple, add complexity as needed."