How Do Enterprises Achieve AI Model Operationalization with ModelOps?

by August 24, 2020

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

Monitoring the effectiveness and performance of a ModelOps program is crucial. ModelOps involves development, testing, deployment and monitoring — all of which can be effective only if it’s making progress in providing the scale and accuracy an organization needs.

McKinsey estimates that the total annual value generated by analytics and AI is between $9.5 trillion and $15.4 trillion. However, a large portion of this potential value could be lost if analytical models aren’t pushed into production.

Due to inefficiencies that slow down the process, many analytics models never make it to “the last mile.” ModelOp, a company that specializes in ModelOps, cites the following technical challenges organizations face in deploying a model into production and offers recommendations:

  • 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.

As the model progresses to production, it is typically exposed to larger volumes of data and data transport modes. The application and IT team will need tools for monitoring and solving performance and scalability challenges. Adopting a consistent, microservices-based approach to production analytics can help solve scalability challenges.