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RAG continues to rise
Practical AI: Machine Learning, Data Science, LLM
Challenges in Evaluating Machine Learning Models
Evaluating machine learning models is challenging due to the lack of consensus on data used and evaluation methods. Classic datasets and evaluation criteria do not hold up well in practice, leading to the need for individually created datasets for testing, which is both difficult and expensive on a larger scale. Many organizations resort to using human-curated data for evaluation, with 42 percent using self-created datasets. Apart from the monetary costs, the evaluation process incurs significant iteration time, especially when running against APIs with variable latencies, leading to slow iterations and hindering the ability to try and test multiple scenarios efficiently even with ample budgetary resources.
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