Lesson 44 • Advanced
MLOps: Pipelines, CI/CD & Versioning ⚙️
Automate the entire ML lifecycle — from experiment tracking to automated retraining pipelines and model versioning.
What You'll Learn in This Lesson
- • Track experiments reproducibly with MLflow-style logging
- • Build automated ML pipelines with validation gates
- • Version data with DVC alongside Git for code
- • Manage model lifecycle: staging → production → archived
- • Run A/B tests to safely promote new models
🎯 Real-World Analogy: MLOps is like a car manufacturing plant. Data scientists are designers who prototype new models. MLOps engineers build the assembly line that produces, tests, and delivers those models at scale — reliably, every time.
1️⃣ The MLOps Lifecycle
MLOps bridges the gap between "it works on my laptop" and "it serves 10M users":
Data → Feature Store → Training → Evaluation → Registry → Deploy → Monitor ↑ | └──────────────── Automated Retraining Trigger ←──────────────────────┘
| Component | Tool | Purpose |
|---|---|---|
| Experiment Tracking | MLflow, W&B | Log params, metrics, artifacts |
| Data Versioning | DVC, LakeFS | Track dataset versions |
| Pipeline Orchestration | Airflow, Kubeflow | Automate train→deploy |
| Feature Store | Feast, Tecton | Reusable feature pipelines |
| Model Registry | MLflow Registry | Version + lifecycle management |
Try It: Experiment Tracking
Log hyperparameters and metrics across multiple model runs, then find the best one
Python
import numpy as np
import json
from datetime import datetime
# ============================================
# EXPERIMENT TRACKING WITH MLFLOW-STYLE LOGGING
# ============================================
np.random.seed(42)
print("=== ML Experiment Tracking ===")
print()
print("Without experiment tracking, you lose track of which")
print("hyperparameters produced which results. MLflow solves this.")
print()
class ExperimentTracker:
"""Simplified MLflow-style experiment tracker."""
def _
...Try It: Automated ML Pipeline
Build a multi-stage pipeline with validation gates that halts on failure
Python
import numpy as np
# ============================================
# ML PIPELINE AUTOMATION (CI/CD for ML)
# ============================================
np.random.seed(42)
print("=== Automated ML Pipeline ===")
print()
print("An ML pipeline automates: data → train → evaluate → deploy")
print("Think of it like a factory assembly line for models.")
print()
class MLPipeline:
"""Simplified ML pipeline with stages."""
def __init__(self, name):
self.name = name
self.stages =
...Try It: Model Versioning & A/B Testing
Register model versions and run statistically rigorous A/B tests
Python
import numpy as np
import json
# ============================================
# MODEL VERSIONING & A/B TESTING
# ============================================
np.random.seed(42)
print("=== Model Registry & Versioning ===")
print()
class ModelRegistry:
"""Track model versions and their lifecycle stages."""
def __init__(self):
self.models = {}
def register(self, name, version, metrics, stage="staging"):
key = f"{name}/{version}"
self.models[key] = {
...⚠️ Common Mistakes
⚠️
Training in notebooks, deploying manually — if it's not in a pipeline, it's not reproducible.
⚠️
Not versioning data — changing data without tracking breaks reproducibility forever.
💡
Pro Tip: Start with MLflow + DVC. They're free, integrate with Git, and cover 80% of MLOps needs.
📋 Quick Reference — MLOps
| Concept | Command / Tool |
|---|---|
| Track experiment | mlflow.log_param(), mlflow.log_metric() |
| Version data | dvc add data.csv → dvc push |
| Register model | mlflow.register_model() |
| Promote model | transition_model_version_stage("Production") |
| A/B test | Route 90/10 traffic, check z-score > 1.96 |
🎉 Lesson Complete!
You've mastered MLOps fundamentals! Next, learn how to build recommendation systems that power Netflix, Spotify, and Amazon.
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