MLO stands for "Machine Learning Operations." It refers to the field and practices that encompass managing and deploying machine learning (ML) models in production environments. MLO focuses on the operational aspects of ML, ensuring that models can be integrated, maintained, and scaled within real-world applications effectively.
In a broader sense, MLO can also be defined as the set of processes, tools, and technologies used to streamline and automate the entire ML lifecycle. This includes tasks such as data collection and preprocessing, model training, validation, deployment, monitoring, and maintenance.
MLO often involves collaboration between data scientists, data engineers, and IT professionals. Its core objective is to bridge the gap between ML research and application by creating a structured framework that allows organizations to deploy and use ML models reliably and efficiently.
Key components of MLO include version control and reproducibility, model serving and deployment, infrastructure management, and model monitoring and retraining. These processes help ensure that ML models are reliable, up-to-date, and provide accurate predictions.
MLO has gained importance as ML models become increasingly complex and more organizations invest in their integration. It provides organizations with the necessary tools and practices to successfully manage ML models in production, ultimately maximizing their value and enabling the successful implementation of ML-based solutions.
