Machine Learning Operations (MLOps) is a set of practices and tools designed to streamline the entire lifecycle of machine learning models, from development to deployment and maintenance. It aims to integrate machine learning with DevOps principles to ensure that models are reliable, scalable, and efficient in production environments.
-
Development: MLOps focuses on creating a seamless workflow for developing machine learning models. This includes data preprocessing, feature engineering, model building, and training. The goal is to ensure that models can be developed quickly and efficiently. See Machine Learning Workflow.
-
Deployment: Once a model is developed and evaluated, MLOps facilitates its deployment into a production environment. This involves setting up the necessary infrastructure to serve the model and ensuring that it can handle real-world data and workloads.
-
Maintenance: MLOps emphasizes the importance of monitoring and maintaining models over time. This includes tracking model performance, detecting data drift, and retraining models as needed to ensure they remain accurate and relevant.
-
Generalization and Robustness: MLOps aims to create models that generalize well to new, unseen data, especially in dynamic environments. It also focuses on ensuring models remain robust to noisy or unexpected data inputs.
-
Collaboration and Automation: MLOps encourages collaboration between data scientists, engineers, and operations teams. It also leverages automation to streamline repetitive tasks, such as model training, evaluation, and deployment.
-
Model Observability Model Observability and Retraining: Continuous monitoring of model performance is crucial in MLOps. Observability tools help track metrics and identify when a model needs retraining due to changes in data patterns or performance degradation.