Open Internet by MindsNet

Deploying ML Models that Stick

Many ML projects feel complete but are rarely used after deployment. This happens when models don't fit into existing workflows, outputs are hard to access, or produce too many signals that are ignored. The issue seems to stem from treating deployment as the finish line, rather than the starting point for real-world usage.

Computing & Technology, Computer Science, Machine Learning