⚙️ AI Engineering Week 1 of 14 BSc · Y3 ⏱ ~50 min

Week 1: Model Versioning, Deployment Strategies & CI/CD

Deploy ML models reliably in production: versioning with MLflow, automated CI/CD, canary deployments, A/B testing, and drift detection pipelines.

University of Aliens

AIE301 — Lecture 1 · BSc Y3

🎬 CC Licensed Lecture

0:00 / —:—— 📺 Creative Commons Licensed

🎯 Learning Objectives

Implement blue-green and canary deployment strategies for ML models
Build automated retraining pipelines triggered by drift alerts
Design model serving infrastructure (batch, online, streaming)
Set up comprehensive model monitoring with alerting

Topics Covered This Lecture

Deployment Strategies: Blue-Green, Canary, Shadow

Model Serving: REST, gRPC, Streaming

Automated Retraining Pipelines

Drift Detection & Alerting

📖 Lecture Overview

This first lecture establishes the foundational framework for MLOps & Model Deployment. By the end of this session, you will have the conceptual grounding and practical starting point needed for the rest of the course.

        Why this matters
        Deploy ML models reliably in production: versioning with MLflow, automated CI/CD, canary deployments, A/B testing, and drift detection pipelines. This lecture sets up everything that follows — make sure you understand the core concepts before proceeding to Week 2.
      

Key Concepts

The lecture introduces the four main pillars of this course: Deployment Strategies: Blue-Green, Canary, Shadow, Model Serving: REST, gRPC, Streaming, Automated Retraining Pipelines, Drift Detection & Alerting. Each will be explored in depth over the 14-week curriculum, with hands-on projects reinforcing theory at every stage.

# Quick Start: verify your environment is ready for AIE301
import sys
print(f"Python {sys.version}")

# Check key libraries are installed
try:
    import numpy, pandas, matplotlib
    print("✅ Core libraries ready")
except ImportError as e:
    print(f"❌ Missing: {e} — run: pip install numpy pandas matplotlib")

This Week's Focus

Focus on mastering: Deployment Strategies: Blue-Green, Canary, Shadow and Model Serving: REST, gRPC, Streaming. These are the prerequisites for everything in Week 2. The concepts build on each other — do not skip the practice exercises.

📋 Project 1 of 3 50% of Final Grade

AIE301 Project 1: Full MLOps Pipeline

Build a complete MLOps pipeline for a fraud detection model: automated training, canary deployment, A/B testing framework, drift monitoring, and auto-remediation.

Canary deployment with traffic splitting
Automated retraining trigger on data drift
A/B test framework with statistical significance tracking
Runbook for model incidents

50%

3 Projects

20%

Midterm Exam

30%

Final Exam

📝 Sample Exam Questions

These represent the style and difficulty of questions you'll see on the midterm and final. Start thinking about them now.

Conceptual Short Answer

Explain the difference between blue-green deployment and canary deployment for ML models.

Analysis Short Answer

What is shadow deployment? When would you use it instead of canary?

Applied Code / Proof

Design a data drift detection system that triggers model retraining automatically.