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The Art and Science of Training LLMs // Bandish Shah and Davis Blalock // #219

MLOps.community

NOTE

Challenges and Pitfalls in Data Pipelines

Data engineering involves challenges such as difficulty in obtaining and cleaning data, tokenization issues leading to invalid outputs, challenges in loading data at scale, requirements around deduplication, shuffling issues causing convergence problems, resumption challenges after job crashes, and the need for using specialized libraries to navigate the subtle pitfalls. Scaling up exacerbates these challenges, emphasizing the importance of efficient data handling to avoid performance bottlenecks and operational complexities.

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