📊 Data Science Week 1 of 14 MSc · S2 ⏱ ~50 min

Week 1: CI/CD for ML, Feature Stores & Model Monitoring

Deploy and maintain ML systems in production: CI/CD pipelines, feature stores, model registries, A/B testing frameworks, and drift detection.

University of Aliens

DS506 — Lecture 1 · MSc S2

🎬 CC Licensed Lecture

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

🎯 Learning Objectives

Build an end-to-end ML CI/CD pipeline with GitHub Actions
Implement a feature store using Feast or Tecton
Set up model monitoring with drift detection alerts
Design and analyze an A/B test for model deployment

Topics Covered This Lecture

ML CI/CD with GitHub Actions

Feature Stores: Feast, Tecton

Model Monitoring & Drift Detection

Canary Deployments & A/B Testing

📖 Lecture Overview

This first lecture establishes the foundational framework for MLOps & Production Systems. 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 and maintain ML systems in production: CI/CD pipelines, feature stores, model registries, A/B testing frameworks, and drift detection. 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: ML CI/CD with GitHub Actions, Feature Stores: Feast, Tecton, Model Monitoring & Drift Detection, Canary Deployments & A/B Testing. 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 DS506
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: ML CI/CD with GitHub Actions and Feature Stores: Feast, Tecton. 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

DS506 Project 1: Production ML Platform

Build a complete MLOps platform for a classification model: automated retraining pipeline, feature store, model registry, monitoring dashboard, and canary deployment.

CI/CD pipeline (GitHub Actions or MLflow)
Feature store integration (Feast or Hopsworks)
Monitoring dashboard with drift alerts
Canary deployment with A/B test analysis

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

What is the difference between data drift and concept drift? How do you detect each?

Analysis Short Answer

Describe the components of a feature store. Why is a feature store important for large ML teams?

Applied Code / Proof

How do you design a statistically valid A/B test for comparing two ML models in production?