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Software Systems Engineering

Bridge the gap between theory and production. Learn to architect scalable, fault-tolerant distributed systems. From advanced database design and microservices patterns to CI/CD pipelines and observability, this course prepares you to build the infrastructure that powers modern AI applications.

Fundamental Concepts of Software Systems Engineering (Step-by-Step)

From full-stack → Next.js → system design → microservices → ML → agents → production AI product.

STEP 1 — Build the Base Web Product (React + Flask + DB)

(Weeks 1–2) Your very first foundation: a real app that everything else builds on.

  • Frontend (React fundamentals): JSX, Hooks, Controlled forms, Client-side routing, Context API, Data fetching.
  • Backend (Flask): REST endpoints, Request validation, SQLAlchemy ORM, Auth, Error handling, DB design.
  • Testing: React Testing Library, Pytest.
  • Deployment: Vercel (Frontend), Render/Heroku (Backend).
  • Deliverable: User + Posts + Comments product built on React + Flask + Postgres.

STEP 1.5 — Upgrade to Next.js (Full-Stack React + SSR/ISR/Edge)

(Week 2.5) Transform your React SPA into a scalable, high-performance Next.js app.

  • Concepts: App Router, Server vs Client Components, SSR/SSG/ISR, API routes, Edge middleware, Image optimization.
  • Integration: Auth pages (SSR), User profiles (ISR), Feed (Client + Streaming), API proxy.
  • Deliverable: Next.js frontend + Flask backend.

STEP 2 — Add Architecture Fundamentals

(Week 3) Architect your existing app like a senior engineer.

  • Analysis: Latency heatmap, Scalability constraints, Caching opportunities, Database bottlenecks.
  • Deliverable: Architecture document (UML, Request flows, Caching plan, Scaling plan).

STEP 3 — Add Classic Distributed System Building Blocks

(Week 4) Extend your platform from "web app" to "distributed system".

  • Caching Layer (Redis): Cache-aside pattern, Session storage, Rate limiting.
  • Async Workers (Celery/BullMQ): Offload heavy tasks (e.g., image resizing, email sending).
  • Load Balancing: Nginx basics, Round-robin vs Least connections.
  • Deliverable: App with Redis caching and background workers.

STEP 4 — Add Search & Analytics (Elasticsearch + ClickHouse)

(Week 5) Handle data at scale.

  • Search Engine: Indexing data in Elasticsearch, Fuzzy search, Faceted search.
  • Analytics: Tracking user events, ClickHouse for OLAP, Building a dashboard.
  • Deliverable: Search bar with autocomplete + Admin analytics dashboard.

STEP 5 — Add Machine Learning (The "AI" in AI Engineering)

(Week 6) Integrate a predictive model (non-LLM) into your system.

  • Model Building: Scikit-learn basics, Training a simple recommender or classifier.
  • Model Serving: ONNX runtime, FastAPI for inference, Batch vs Real-time inference.
  • Integration: Show recommended posts in the feed.
  • Deliverable: "Recommended for you" widget powered by a real ML model.

STEP 6 — Add Generative AI (LLMs & RAG)

(Week 7) The modern AI stack.

  • LLM Integration: OpenAI API / Anthropic API, Prompt engineering, Streaming responses.
  • RAG Pipeline: Vector DB (Pinecone/Weaviate), Embeddings, Retrieval, Context injection.
  • Feature: "Chat with your posts" or "AI Summary" button.
  • Deliverable: GenAI feature live in production.

STEP 7 — Add AI Agents

(Week 8) From passive chatbot to active agent.

  • Agent Architecture: Tools (Search, Calculator, DB access), Planning (ReAct), Memory.
  • Frameworks: LangChain / LangGraph basics.
  • Feature: "Personal Assistant" that can search your posts and draft replies.
  • Deliverable: Agent capable of multi-step reasoning.

STEP 8 — Production Readiness (DevOps & MLOps)

(Week 9) Make it robust, observable, and scalable.

  • CI/CD: GitHub Actions pipelines for test & deploy.
  • Observability: Logging (ELK/Loki), Metrics (Prometheus/Grafana), Tracing (OpenTelemetry).
  • MLOps: Model versioning, Eval pipelines, Drift detection.
  • Deliverable: Fully instrumented production system.
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