This chapter focuses on the critical role of data evaluation in machine learning, particularly the necessity of reserving portions of data for model testing. It explores the use of benchmark datasets for refining and assessing model performance, while also considering the unique challenges posed by specific industry tasks. Additionally, the chapter discusses the integration of retrieval augmented generation within generative AI models and the importance of understanding data processes for effective deployment.
You might have heard that “AI is only as good as the data.” What does that mean and what data are we talking about? Chris and Daniel dig into that topic in the episode exploring the categories of data that you might encounter working in AI (for training, testing, fine-tuning, benchmarks, etc.). They also discuss the latest developments in AI regulation with the EU’s AI Act coming into force.
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