In the current AI landscape, the hype cycle is undeniably focused on large language models (LLMs). From code generation to reasoning, models like GPT-4 and Llama 3 have transformed how we interact with data. However, for machine learning (ML) engineers tasked with building robust, production-grade pipelines for tabular data or predictive analytics, LLMs are not always the silver bullet.
Automated Machine Learning (AutoML) has quietly matured into a powerhouse technology, automating the tedious aspects of data science — feature engineering, model selection, and hyperparameter tuning.
Read More from DZone.com Feed