Is ModelOps important to manage AI/ML models? – By Aditya Abeysinghe
What is ModelOps?
Machine learning (ML) is used in many platforms today. ML modelling algorithms and ML-based programming have made it easier over the last decade to find the best ML model for a given app by training data. Different stages are used in the deployment of a ML model or an Artificial Intelligence (AI) model. Finding suitable datasets, preparing data chosen to train models, training models, testing models, deploying the model, and monitoring the deployed model are some common stages used. These stages of deploying and monitoring models is called Model Operationalization (ModelOps).
Why ModelOps?
As each phase of the deployment of models is monitored by ModelOps, issues in a phase can be identified earlier and mitigated before they are passed to other phases. This could reduce risks and costs associated with rectifying issues in later phases. Also, a deployment could be stopped if an issue is found early reducing effort and minimizing issues of models before they are made publicly available. It also reduces loss of reputation for businesses as products with less issues are available for users.
For most AI-driven organizations, several teams are involved in preparing and transforming data, and model training. Often conflicts arise between teams when there are communication issues and less management of teams. ModelOps reduces issues in teams used for the deployment of models by managing and monitoring the workflow. Also, goals are assigned for each role which reduces gaps in delivering the deliverable by each team role.
ModelOps can reduce costs of model development by reducing overhead time and costs when progressing between different stages of the deployment. Since each model that is used in deployment is monitored, the costs for analyzing the efficiency of models for production is reduced. Issues of the current cycle are automatically used for the next cycle and the costs for additional analysis of issues is thus minimized.
ModelOps is not limited to moving between different stages of deploying ML models. It also focuses on automation of monitoring, managing, and maintaining quality of models. Monitoring models is often done using monitoring dashboards and analytics systems. Models are automatically tested when a large user base use these models to reduce time to deploy.
Issues with ModelOps
ModelOps is not only using several stages to manage AI model deployment and monitoring models. It also requires tools and expert teams to initially transform model management from traditional to a ModelOps-based method and to manage this new method. Tools used to manage stages and analyze the stage deliverables are often costly and the cost for expertise on ModelsOps is high for small and medium scale teams. Therefore, for these organizations ModelOps is not feasible to be used in model management.
