Machine learning (ML) is becoming an increasingly important part of the modern application stack. Whether it’s large-scale, public large language models (LLM) like GPT or small-scale, private models trained on company content, developers need to find ways of including those models in their code.
That means finding ways to test that code, without pushing it to production servers. We can build on some of the MLOps concepts used to manage the underlying models, merging them with familiar devops techniques. However, much of that work is bespoke, with developers constructing their own toolchains and building their own test harnesses. As teams change, the wheel gets reinvented, again and again.
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InfoWorld
