Software Engineering for Machine Learning

16 lectures

Engineering practices, testing, and MLOps that take models from notebook to production.

01Lecture 01 · Software Engineering for Machine Learning — Foundation and Overview

02Lecture 02 · Machine Learning Foundations for Software Engineering

03Lecture 03 · Requirements Engineering and ML System Architecture

04Lecture 04 · GR for ML, Quality Attributes, and System Architecture

05Lecture 05 · Architectural Patterns, CQRS, and Retrieval-Augmented Generation

06Lecture 06 · Monolith, Microservices, Event-Driven Architecture, and Model Registry

07Lecture 07 · Design Patterns for ML Systems: Registry, Serving, and Agentic Orchestration

08Lecture 08 · Saga Pattern and Architectures for Agentic AI

09Lecture 09 · Coding Practices and Code Performance Analysis for ML

10Lecture 10 · Time Complexity, Data Structures, and OOP for ML Systems

11Lecture 11 · Implementation and Code Sharing — Part 2

12Lecture 12 · Quality Assurance and System Quality for Machine Learning

13Lecture 13 · Docker and Kubernetes for ML Deployment

14Lecture 14 · Kubernetes Architecture and Deployment Strategies

15Lecture 15 · Responsible ML Engineering and LLMOps — End-to-End Ops and Quality Attributes

16Lecture 16 · Responsible AI Principles and Course Review

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