Models are easy. Maintaining them in production is where the real challenge starts.

Machine learning experiments in notebooks are easy — but deploying stable, repeatable models in production requires MLOps. This includes versioning datasets, monitoring drift, automating retraining, and managing deployment workflows.

Platforms like Vertex AI, SageMaker, and MLflow make this easier, but the core principles remain:

• Track every model version and dataset snapshot
• Automate training and evaluation pipelines
• Monitor model performance continuously
• Roll back fast when accuracy drops

MLOps turns ML from a research activity into an operational system that delivers consistent business value.