Soft Landing Program
Time Commitment & Expectations
Estimated Time Impact
174-251 hours total
Soft-Landing Prerequisites
| Section | Learning Source | Public Testing Source |
|---|---|---|
| Command Line Mastery | Linux Journey - Command Line Module | Certificate Test Available |
| Problem-Solving Fundamentals | HackerRank: Problem Solving (Basic) | Certificate Test Available |
| Calculus 1 - Khan Academy | Calculus 1 - Khan Academy | Final Exam Available |
| Linear Algebra - Khan Academy | Linear Algebra - Khan Academy | Final Exam Available |
| Statistics and Probability | Statistics and Probability - Khan Academy | Final Exam Available |
| Pandas & Data Cleaning | Kaggle: Pandas Micro-Course | Certificate Test Available |
| SQL | SQL Skills (Intermediate) - HackerRank | Certificate Test Available |
| Software Engineering Tools | Git Branching Game | Certificate Test Available |
Please refer to the Engineering Fundamentals documentation for detailed instructions on completing the prerequisites.
Welcome to the Deep End
If you're here, one of two things is true:
- You completed Engineering Fundamentals and proved your foundations
- You're an experienced developer who tested into Soft Landing directly
Either way: congratulations. You're about to transform from someone who can code to someone who can build AI systems.
Why It's Called "Soft Landing"
The name comes from aerospace engineering. When a spacecraft lands on another planet, a "soft landing" means:
- Controlled descent - Not crashing, but not staying in orbit either
- Gradual adjustment - Adapting to new gravity, atmosphere, conditions
- Safe touchdown - Ready to explore the new terrain
That's exactly what this phase does for you.
You've been in "orbit" - learning theory, following tutorials, building toy projects. Now you're landing in the real world of professional AI engineering:
- Real datasets (messy, incomplete, biased)
- Real constraints (time, compute, deployment)
- Real systems (networking, Docker, cloud infrastructure)
- Real expectations (code reviews, documentation, production quality)
The "soft" part? We guide you through it with structure and support so you don't crash and burn.
The Two Stages of Soft Landing
- Stage 1: Core Systems
- Stage 2: Specialization Tracks
Stage 1: Core Systems (Months 1-3)
Goal: Master the fundamental building blocks of AI engineering.
ML Fundamentals
Classical ML, Evaluation metrics.
Deep Learning
PyTorch, Transformers, Training loops.
Systems & Networking
Linux, Docker, API Design, HTTP.
Fullstack Toolkit
FastAPI, Databases, CI/CD, Cloud.
Time Investment: 74-101 hours
Outcome: You can build, train, and deploy ML models to production.
Stage 2: Specialization Tracks (Months 3-6)
Goal: Deep expertise in one AI domain.
Before selecting your specialization track, discuss your career goals, interests, and background with a mentor. This ensures you choose the path that best aligns with your professional objectives and market demands.
Choose Your Path:
- Computer Vision – Image classification, Object detection, Visual intelligence
- Data Science – Statistical modeling, A/B testing, Analytics
- NLP & LLMs – Transformers, RAG, Agents, Fine-tuning
- Generative AI – Diffusion models, Speech technologies, Text-to-Speech
- AI Software Engineering – Distributed systems, Scalability, Production ML
Time Investment: 100-150 hours per track
Outcome: Portfolio of 3-5 domain-specific projects proving expertise.