Which term describes practices for managing the lifecycle of machine learning models?

Which term describes practices for managing the lifecycle of machine learning models?

• A. Machine Learning Operations (MLOps)
• B. Learning Curves
• C. Loss Function Analysis
• D. Levels of Autonomy

Answer: (A)

The term that describes practices for managing the lifecycle of machine learning models is Machine Learning Operations, often abbreviated as MLOps. This concept encompasses a set of practices aimed at streamlining the development, deployment, monitoring, and management of machine learning models in production. MLOps aims to improve collaboration between data scientists and operations teams, ensuring that models are not only built effectively but also maintained and scaled efficiently in real-world applications. MLOps includes activities such as version control for models, ensuring reproducibility, automated testing, continuous integration and continuous deployment (CI/CD) practices specific to machine learning, as well as monitoring model performance and data drift over time. By establishing a cohesive framework, MLOps addresses the challenges associated with transitioning models from experimental phases into practical use, managing updates, and maintaining model quality. Other choices like learning curves, loss function analysis, and levels of autonomy focus on more specific aspects of machine learning but do not encompass the comprehensive lifecycle management of models. Learning curves relate to the performance improvement of models over time or in relation to data size; loss function analysis involves evaluating model performance against a specific metric during training; and levels of autonomy pertain to the degree of decision-making capability assigned to an AI system, rather than management practices

The term that describes practices for managing the lifecycle of machine learning models is Machine Learning Operations, often abbreviated as MLOps. This concept encompasses a set of practices aimed at streamlining the development, deployment, monitoring, and management of machine learning models in production. MLOps aims to improve collaboration between data scientists and operations teams, ensuring that models are not only built effectively but also maintained and scaled efficiently in real-world applications.

MLOps includes activities such as version control for models, ensuring reproducibility, automated testing, continuous integration and continuous deployment (CI/CD) practices specific to machine learning, as well as monitoring model performance and data drift over time. By establishing a cohesive framework, MLOps addresses the challenges associated with transitioning models from experimental phases into practical use, managing updates, and maintaining model quality.

Other choices like learning curves, loss function analysis, and levels of autonomy focus on more specific aspects of machine learning but do not encompass the comprehensive lifecycle management of models. Learning curves relate to the performance improvement of models over time or in relation to data size; loss function analysis involves evaluating model performance against a specific metric during training; and levels of autonomy pertain to the degree of decision-making capability assigned to an AI system, rather than management practices