I had a really good conversation with Dean Pleban (CEO @ DAGsHub), who shared some great practical insights based on his own experience helping teams go from experiments to real-world production.
Some practical tips Dean shared with me:
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Save chain of thought output (the output text in reasoning models) - you never know when you might need it. This sometimes require using the verbos parameter.
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Log experiments thoroughly (parameters, hyper-parameters, models used, data-versioning…).
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Start with a Jupyter notebook, but move to production-grade tooling (all tools mentioned in the guide bellow 👇🏻)
To help myself (and hopefully others) visualize and internalize these lessons, I created an interactive guide that breaks down how successful ML/LLM projects are structured. If you’re curious, you can explore it here:
https://www.readyforagents.com/resources/llm-projects-structure
