Day 13: Explaining ML's Neglected Concepts
𝗜𝘀𝘀𝘂𝗲𝘀 𝗼𝗳 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗮𝗹 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 -> Continual learning looks clean in research. Shipping it is a different conversation.
What actually happens:
•Your data stream doesn't tell you when the distribution shifted - you find out when accuracy drops.
•Replay buffers sound simple until legal asks what user data you're storing for retraining.
•The model stabilizes on early tasks, then quietly degrades as new ones pile up.
•Papers benchmark on final accuracy; production cares whether the model still works six months later.
Key issues in practice:
•Elastic Weight Consolidation protection weakens over time as the model drifts away from where those weights mattered.
•Most methods just try not to forget - almost none help old knowledge actually improve new tasks.
•Every replay cycle bakes dataset bias a little deeper into the weights.
What happens in real stacks:
•Healthcare and finance are the obvious Continual Learning use cases. Both ban storing old examples for replay.
•Teams scope the domain tighter and tighter to avoid drift - which is avoiding the problem, not solving it.
•Most shops hit a wall early and go back to full retraining on a schedule.
The research is years ahead of what teams can actually deploy.
Where did CL break down for you - data, privacy, or just the complexity of keeping it stable?