How is ML different from MLOps?

6 of 17 names confirmed · named in 2 or more of 5 answers · 0 engines

Machine Learning (ML) focuses on building models that learn from data to make predictions or decisions, while MLOps (Machine Learning Operations) is about deploying, monitoring, and maintaining those models in production. ML is concerned with model accuracy and development, whereas MLOps ensures model reliability, scalability, and automation. The key difference lies in their primary goals, with ML aiming to maximize model performance and MLOps focusing on ensuring the model runs smoothly in production.