Is Traditional ML Dead?
Date:
- Examined the evolving landscape of machine learning in the era of large language models and foundation models.
- Challenged the narrative that traditional ML is obsolete — argued for its continued relevance in structured data problems, low-latency inference, and resource-constrained environments.
- Discussed when LLMs and generative AI are the right tool versus when classical models (gradient boosting, SVMs, linear models) outperform them in cost, speed, and interpretability.
- Highlighted hybrid architectures that combine traditional ML with LLM components for production systems.
- Addressed practical trade-offs: compute cost, data requirements, explainability, and regulatory compliance that keep traditional ML indispensable.