Definition
MLOps, or Machine Learning Operations, is a set of practices that combines machine learning, development, and IT operations to deploy and maintain machine learning models in production. It streamlines the entire machine learning lifecycle, from data preparation and model training to deployment, monitoring, and retraining. By automating and standardizing these processes, MLOps ensures that machine learning models are reliable, scalable, and performant in real-world applications. For example, an e-commerce platform might use MLOps to continuously update its recommendation engine based on new customer data, improving user experience and sales. This differs from traditional software development by incorporating the iterative nature of model experimentation and the need for continuous retraining. You will encounter MLOps concepts in advanced AI and cloud certification domains, particularly those focused on deploying and managing machine learning solutions.