In this MLflow vs Weights & Biases vs ZenML article, we explain the difference between the three platforms and educate you about using them in tandem too.
A comprehensive exploration of real-world lessons in LLM evaluation and quality assurance, examining how industry leaders tackle the challenges of assessing language models in production. Through diverse case studies, the post covers the transition from traditional ML evaluation, establishing clear metrics, combining automated and human evaluation strategies, and implementing continuous improvement cycles to ensure reliable LLM applications at scale.
Practical lessons on prompt engineering in production settings, drawn from real LLMOps case studies. It covers key aspects like designing structured prompts (demonstrated by Canva's incident review system), implementing iterative refinement processes (shown by Fiddler's documentation chatbot), optimizing prompts for scale and efficiency (exemplified by Assembled's test generation system), and building robust management infrastructure (as seen in Weights & Biases' versioning setup). Throughout these examples, the focus remains on systematic improvement through testing, human feedback, and error analysis, while balancing performance with operational costs and complexity.
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