How Do Enterprises Achieve AI Model Operationalization with ModelOps?
Accelerated delivery of AI products to business users is the ultimate goal of Model Ops
Gartner’s “A Guidance Framework for Operationalizing Machine Learning” describes ModelOps (AI model operationalization) as primarily focused on the governance and life cycle management of AI and decision models. ModelOps enables the retuning, retraining or rebuilding of AI models, providing an uninterrupted flow between the development, operationalization and maintenance of models within AI-based systems.” By 2023, 70% of AI workloads will use application containers or be built using a serverless programming model necessitating a DevOps culture, says the research major. In this juncture, the role of ModelOps is increasingly gaining new heights. ModelOps is a principled approach to operationalizing a model in apps. ModelOps synchronizes cadences between the application and model pipelines. With multi-cloud ModelOps an enterprise can optimize their data science and AI investments using data, models and resources from edge to the cloud. ModelOps covers the end-to-end lifecycles for optimizing the use of models and applications, targeting machine learning models, optimization models and other operational models to integrate with Continuous Integration and Continuous Deployment (CICD) across the multivariate cloud structure. The ModelOps team assists to bring together a fair communication that exists between data scientists, data engineers, application owners and infrastructure owners. The aim is to coordinate proper handoffs and execution so that models can advance to the so-called “last mile.”ModelOps Responsibilities-
- Workflow automation
- Version management
- Complete resource management
- The analytics model must be compatible from the creation environment to the production environment. An agnostic scoring engine designed to take models created in any language and deploy them into production can help address the challenge of model compatibility across the analytics lifecycle.
- The model must be portable. Docker and other container technologies can help solve the application portability challenge by capturing the environmental dependencies for the analytic workload, providing a portable image.
- Monolithic and locked-in platforms may limit what organizations can do or offer services companies don’t need. However, containerization technologies can help organizations to use native microservice software to address changing needs and limit service failures to isolated components.