Evaluating Data Systems
Establishing a source of truth is crucial for evaluating data systems, yet many overlook this step. Writing tests for code parallels the need for an evaluation set in data projects, as both ensure reliability and accuracy. The process of deciding how to label and categorize data is often complex and requires experimentation, highlighting the importance of a well-structured pipeline, especially when utilizing libraries designed for production efficiency.In this clip
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Software Engineering Radio - the podcast for professional software developers
SE Radio 611: Ines Montani on Natural Language Processing
Related Questions
What are some techniques for training machine learning models as discussed in the episode Ines & Sofie — Building Industrial-Strength NLP Pipelines and the clip Continuous Model Improvement?
What challenges are faced in training large language models (LLMs) as discussed in the episode Jennifer Prendki Interview - Agile Machine Learning - TWiML Talk #46 and the clip Monitoring NLP Models?
How does natural language processing work?