As Large Language Models (LLMs) revolutionize software development, the challenge of ensuring their reliable performance becomes increasingly crucial. This comprehensive guide explores the landscape of LLM evaluation, from specialized platforms like Langfuse and LangSmith to cloud provider solutions from AWS, Google Cloud, and Azure. Learn how to implement effective evaluation strategies, automate testing pipelines, and choose the right tools for your specific needs. Whether you're just starting with manual evaluations or ready to build sophisticated automated pipelines, discover how to gain confidence in your LLM applications through robust evaluation practices.
MLOps isn't just about new technologies and coding practices. Getting better at productionizing your models also likely requires some institutional and/or organisational shifts.
The MLOps movement and associated new tooling is starting to help tackle the very real technical debt problems associated with machine learning in production.
Using config files to specify infrastructure for training isn't widely practiced in the machine learning community, but it helps a lot with reproducibility.
Software engineering best practices have not been brought into the machine learning space, with the side-effect that there is a great deal of technical debt in these code bases.
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