name	mlops
category	ml-ai
difficulty	advanced
estimated_time	60 minutes
prerequisites	machine-learning, docker-containers, kubernetes-basics
learning_outcomes	Design end-to-end ML pipelines from training to production, Implement model versioning, experiment tracking, and monitoring, Deploy ML models with batch, real-time, and streaming strategies, Detect and respond to model drift in production, Build reproducible ML workflows with MLOps toolchains
related_skills	data-engineering, ci-cd-pipelines, observability
tags	mlops, machine-learning, model-deployment, ml-pipelines, model-monitoring, mlflow, kubeflow
version	1.0.0
last_updated	Fri Jan 17 2025 00:00:00 GMT+0000 (Coordinated Universal Time)
description	MLOps engineering covering ML pipeline design, model versioning, experiment tracking, deployment strategies, drift detection, and monitoring for production ML systems with tools like MLflow, Kubeflow, and model registries

MLOps (Machine Learning Operations)

Overview

MLOps brings DevOps principles to machine learning workflows, enabling reliable, scalable, and reproducible ML systems in production. It covers the entire ML lifecycle from experimentation to deployment and monitoring.

Core Principles:

Reproducibility: Version everything (code, data, models, environments)
Automation: Automate training, testing, deployment pipelines
- Monitoring: Track model performance, data drift, system health
Collaboration: Bridge data scientists, engineers, and operations
Governance: Ensure model compliance, explainability, and auditing

Level 1: Quick Reference

ML Lifecycle Stages

┌─────────────┐     ┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   Data      │────▶│  Training   │────▶│ Deployment  │────▶│ Monitoring  │
│ Collection  │     │ & Experiment│     │  & Serving  │     │ & Retraining│
└─────────────┘     └─────────────┘     └─────────────┘     └─────────────┘
      │                    │                    │                    │
      │                    │                    │                    │
      ▼                    ▼                    ▼                    ▼
  Versioning        Tracking             Inference          Drift Detection
  Validation        Reproducibility      Scalability        Performance Decay
  Feature Eng.      Hyperparameters      A/B Testing        Alerts & Triggers

MLOps vs Traditional DevOps

Aspect	Traditional DevOps	MLOps
Artifacts	Code, binaries	Code + Data + Models + Features
Testing	Unit, integration tests	Data validation + Model evaluation + Inference tests
Deployment	Deploy once, stable	Continuous retraining, model decay
Monitoring	Logs, metrics, traces	+ Data drift, concept drift, model performance
Versioning	Git for code	Git + DVC for data + Model registry
Reproducibility	Dockerfile, env vars	+ Data versions, random seeds, feature pipelines

Essential MLOps Checklist

Experimentation & Development:

Experiment tracking (MLflow, Weights & Biases, Neptune.ai)
Notebook versioning and parameterization (Papermill)
Feature engineering pipelines with versioning
Data quality checks (Great Expectations)
Model versioning and registry

Training Pipelines:

Automated data validation before training
Reproducible training environments (Docker, Conda)
Hyperparameter tuning (Optuna, Ray Tune)
Distributed training support (Horovod, PyTorch DDP)
Model evaluation metrics logged and tracked

Deployment & Serving:

Model serving infrastructure (TorchServe, TensorFlow Serving, Seldon)
Batch prediction pipelines for offline inference
Real-time inference APIs with latency SLAs
Canary deployments and A/B testing
Shadow mode for model validation

Monitoring & Maintenance:

Data drift detection (statistical tests, KL divergence)
Concept drift detection (model performance degradation)
Prediction monitoring and alerting
Model performance dashboards
Automated retraining triggers
Model rollback procedures

Quick Commands

# MLflow - Track experiments
mlflow ui --host 0.0.0.0 --port 5000
mlflow run . -P alpha=0.5

# Kubeflow - Deploy pipeline
kfp pipeline create --pipeline-name my_pipeline pipeline.yaml
kfp run submit --experiment-name exp1 --pipeline-id <id>

# DVC - Version data
dvc add data/train.csv
dvc push
dvc pull

# Model serving - TorchServe
torch-model-archiver --model-name my_model --version 1.0 --serialized-file model.pt
torchserve --start --model-store ./model_store
curl -X POST http://localhost:8080/predictions/my_model -T input.json

# Feature store - Feast
feast apply
feast materialize-incremental $(date -u +"%Y-%m-%dT%H:%M:%S")

Common Patterns

1. Experiment Tracking

import mlflow

with mlflow.start_run():
    mlflow.log_param("learning_rate", 0.01)
    mlflow.log_metric("accuracy", 0.95)
    mlflow.sklearn.log_model(model, "model")

2. Model Versioning

from mlflow.tracking import MlflowClient

client = MlflowClient()
result = client.create_model_version(
    name="my_model",
    source="runs:/abc123/model",
    run_id="abc123"
)

3. Model Serving

# Load model from registry
model = mlflow.pyfunc.load_model(f"models:/my_model/production")

# Inference
predictions = model.predict(input_data)

4. Drift Detection

from scipy.stats import ks_2samp

# Compare training and production distributions
statistic, p_value = ks_2samp(train_distribution, production_distribution)
if p_value < 0.05:
    trigger_alert("Data drift detected")

Level 2:

📚 Full Examples: See REFERENCE.md for complete code samples, detailed configurations, and production-ready implementations.

Implementation Guide