Is Machine Learning still worth learning in 2026?

This article addresses a reader's question about the future of learning Machine Learning (ML) in 2026, given the growing capabilities of large language models (LLMs). The author argues that classical ML remains valuable due to advantages in data privacy (self-hosting), cost-efficiency for high-volume predictions, suitability for resource-constrained devices, and the need for human expertise in detecting subtle data or methodological errors that LLMs cannot catch. It also emphasizes that LLMs require verification layers for high-stakes applications and cannot autonomously manage the full ML lifecycle, such as monitoring data drift or retraining. The article is a tech-focused career and industry analysis.