Large language models open many new opportunities for data science teams, but enterprise LLM challenges persist—and customization is key.

LLMs have a broad but shallow knowledge, but fall short on specialized tasks. For best performance, enterprises must fine tune their LLMs.

Distillation techniques allow enterprises to access the full predictive power of large language models at a tiny fraction of their cost.

Data labeling remains a core requirement for machine learning projects—especially in the age of genAI and LLMs. Here’s a handy guide.

Jacomo Corbo and Bryan Richardson with QuantumBlack present “Automating Data Quality Remediation With AI” at The Future of Data-Centric AI.

Dr. Ce Zhang is an associate professor in Computer Science at ETH Zürich. He presented “Building Machine Learning Systems for the Era of Data-Centric AI” at Snorkel AI’s The Future of Data-Centric AI event in 2022.