Transitioning from backend to ML engineering isn’t a career restart. Most of your systems thinking, distributed-systems instincts, and production experience transfer directly — the gaps are narrower than they look.
This roadmap is a placeholder. The full piece will cover:
- What’s actually durable from your backend career
- Which ML fundamentals are worth deep study vs. which can stay shallow
- The “ML platform” vs. “ML research” split, and which one you probably want
- A 6–12 month skill-building sequence that keeps you employable throughout
- Hype signals worth ignoring
Coming soon.