Aligning large language models with specific objectives and use cases requires a robust fine-tuning pipeline that ensures both precision and efficiency. Panelists will discuss the technical intricacies of building such pipelines, sharing insights into data curation, model adjustment strategies, and monitoring alignment, while addressing challenges like maintaining model generalization and mitigating risks associated with bias and drift.

Synthetic data is emerging as a game-changing solution to the challenges of data scarcity, privacy concerns, and bias in AI training. Delve into the technical approaches for generating high-quality synthetic data, its potential to solve real-world problems in industries like healthcare and finance, and the engineering challenges of ensuring its effectiveness, reliability, and ethical use in AI-driven systems.