Core Systems Overview
From Fundamentals to Systems Thinking
Soft Landing’s Core Systems phase is built around four tightly connected modules. Together, they take you from math and models to real-world systems and deployment.
The Four Core Systems Modules
1. ML Fundamentals
Build intuition for vectors, matrices, gradients, and classic ML models so you can debug and reason about learning, not just call model.fit().
You Will Focus On:
- Linear algebra & calculus for ML
- Deep learning primitives
- Classical ML and evaluation metrics
2. Intro to AI Engineering
From Notebooks to Real Systems
Turn ideas into production-minded code with PyTorch, LLM architectures, and clean engineering practices.
You Will Focus On:
- Training loops and experiment structure
- Modern deep learning & LLM patterns
- Writing maintainable, testable code
3. Systems & Networking
How the Internet Actually Works
Understand the path from your model to the user: networks, protocols, and servers that move data and responses.
You Will Focus On:
- HTTP, DNS, and core internet plumbing
- Linux servers, SSH, and observability basics
- Debugging real-world connectivity issues
4. Infra Basics
From Model to Product
Learn how to wrap models in APIs, connect them to frontends, and ship them with modern DevOps tools.
You Will Focus On:
- FastAPI-based inference services
- Simple UIs that call your models
- Containerization and basic deployment
Where This Fits in Soft Landing
Once you complete the Core Systems modules, you will:
- Have the math, modeling, and systems foundation to tackle any specialization
- Be able to move models from notebooks to real infrastructure
- Be ready to start a specialization track and a production-grade capstone
