Training checkpoints just became live production systems
The gap between training and deployment is disappearing, and we're not ready for the operational nightmare that's coming.
We’re watching the boundary between model training and production deployment dissolve in real time. Training runs no longer end with a checkpoint upload. They flow directly into inference infrastructure, turning every gradient update into a potential production incident.
The old boundaries are gone
Model training used to be a batch process. You trained, validated, packaged, deployed. There were gates between each stage. Now we have continuous training pipelines that push model updates to production APIs whilst the training run is still active. The deployment process has been absorbed into the training loop itself.
This isn’t just about faster iteration. It’s about infrastructure that treats training checkpoints as versioned services. Your model server doesn’t load a static file anymore. It subscribes to a training stream.
Nobody planned for this operational complexity
We built our monitoring, logging, and incident response around the assumption that models were stable artefacts. Now we need to debug why inference quality dropped at 3 AM and trace it back to a specific batch that hit the training cluster six hours earlier. Traditional DevOps tools weren’t designed for systems where the code literally rewrites itself every few minutes.
The real problem isn’t technical. It’s that we’re treating neural networks like microservices when they behave more like biological systems. You can’t just roll back a model update like you roll back a code deployment.