Deploying ML & LLM Projects: Lessons From DAGsHub CEO Dean Pleban
Deploying ML & LLM projects? Data quality matters more than model architecture. Use robust data handling, frameworks like RAES, and log experiments thoroughly. Start with Jupyter notebooks, then move to production-grade tooling.
Hey guys! I've been experimenting with building and deploying ML and LLM projects for a while now, and honestly, it’s been a journey. Training the models always felt more straightforward, but deploying them smoothly into production turned out to be a whole new beast. Recently, I had a really good conversation with Dean Pleban (CEO @ DAGsHub), who shared some incredibly practical insights based on his own experience helping teams go from experiments to real-world production. Here are a few valuable lessons Dean shared with me that I think could help others as well: Data matters way more than...