Model Management refers to the systematic and strategic handling of various types of models within a given context, such as in the fields of business, technology, or data science. In business, models can include anything from financial models and marketing models to operational or organizational models. Effective model management involves the careful development, deployment, monitoring, and updating of these models to ensure their accuracy, relevance, and alignment with organizational goals. In technology and data science, model management is crucial for handling machine learning models, algorithms, and analytical models. This includes version control, model testing, and ongoing optimization to adapt to changing data patterns. The key elements of successful model management include documentation, collaboration, and a robust infrastructure that supports model lifecycle management. With the rapid advancements in technology and the increasing reliance on data-driven decision-making, proper model management practices are essential for organizations to harness the full potential of their models and stay agile in today's dynamic environments
1.1 Models Management
The purpose of this document is to outline the specifications for a Model Management System, which is a critical component in the lifecycle of machine learning models. This system is designed to facilitate the efficient development, deployment, monitoring, and maintenance of models within an organization.
1.2 Scope
The Model Management System encompasses the entire lifecycle of machine learning models, from the initial development and training phase to deployment, monitoring, and eventual retirement. It covers aspects such as version control, collaboration, deployment management, and performance monitoring.
