What is the difference between ML & MLOPS?

Machine Learning (ML) is about teaching computers to learn from data and make predictions or decisions. It involves steps like preparing data, building models, and training them.

MLOps (Machine Learning Operations) is about managing and running these ML models once they are ready. It makes sure the models are deployed correctly, monitored closely, and updated when needed to work well in real life.

In short:

ML = Building and training smart models.

MLOps = Keeping those models working smoothly in the real world.

Without MLOps, ML models might not work properly after deployment. MLOps uses automation, monitoring, and teamwork to make sure models stay reliable and deliver real value.