MLOps: Bridging the Gap Between Development and Operations

MLOps is basically DevOps but with extra headaches. It’s about automating the entire ML lifecycle: from data ingestion to model training to deployment and monitoring. I used to manually copy models to servers, which was error-prone and slow. Now I use CI/CD pipelines for models. It sounds boring compared to tuning algorithms, but it’s arguably more valuable. If you can’t reliably and repeatedly get your models into production, all that cool research doesn’t matter.