![]() ![]() This decay affects all three parts of the business calculations, ensuring the realistic nature of the model. To simulate this, a decay factor is added, gradually degrading the performance of each model. In the simulation, the three components work together, and one important element in this is the model’s predictive power, which decays from day one. The value and cost generated by each model depends on three factors The simulation, therefore, helps businesses understand the value and impact of ModelOps practices and their associated costs. Aggregating these factors across all models yields the total net value of the model portfolio. Three components are used to build that total value and cost: user base, prediction volume, and value and cost per prediction. The simulation aims to demonstrate the value and impact of possibly implementing ModelOps practices and to understand the generated value and cost of the model portfolio. This is essential to see its duration online, predictions made, total value, and generated costs. Tracking of the model's statistics is also available in the simulation. Periodical retraining is usually scheduled where the model is not necessarily taken offline, but there are two scenarios where this may happen: an issue requiring a developer to repair and bring it back online, or a drift event causing performance decay. Once in production, the model spends most of its time online making predictions. In the simulation, each model is represented as an agent with a statechart to track its operation status. The process flow of a typical lifecycle of an AI/ML model Priorities three and four are to deploy new models or build entirely new ones. Regular retraining is prioritized second, with each model having a specific schedule. At the same time, there is also a focus on having models online and operational as much as possible. The priority is to fix issues and take models offline. The development team, as resources within the model, prioritizes these activities. The focus is on activities during production, whether online serving predictions or offline doing retraining. This is a process flow describing the typical lifecycle of an AI or ML model, starting with an idea backlog, followed by model building and deployment, production, and eventual retirement or obsolescence. ![]() All of these are crucial, so let’s explore them in more detail now. To manage their portfolio of models, PwC devised a four-part solution that included the model lifecycle, the models themselves, usage dynamics, and ModelOps capabilities. Assist clients in experimenting with and testing various probabilities as a decision-making tool.Integrate actual customer data to quantify outcomes.Show the value and cost of adding different ModelOps functionalities.Reflect the dynamics of a development team.To implement ModelOps, PwC used AnyLogic to develop a simulation model that would track their model portfolio. It includes activities such as initial model deployment, ongoing automated monitoring, evaluation and performance check-ins, and continuous retraining and redeployment. It is often used to identify opportunities to improve the value, quality, and efficiency of machine learning models at every stage of the model lifecycle and prevent value decay. This helps companies reduce the risk of model failure, optimize model performance, and improve the overall success of their machine learning projects. ModelOps enables organizations to manage their models throughout their lifecycle, from development and testing to deployment, monitoring, and maintenance. ![]() It involves a combination of people, processes, and technology to ensure that the models remain accurate and reliable over time. ![]() It is a practice that focuses on scaling and deploying machine learning models in production environments. More and more companies are using advanced machine learning models in their businesses, and as a result, they face many challenges, including how to manage these models throughout their lifecycles. ![]()
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