What is ModelOps?
A critical capability to Scale and Govern Enterprise AI
“ModelOps (AI model operationalization) is primarily focused on the governance and life cycle management of AI and decision models (including machine learning, knowledge graphs, rules, optimization, linguistic and agent-based models). Core capabilities include the management of model development environments, model repository, champion-challenger testing, model rollout/rollback, and CI/CD integration. 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. ModelOps provides business domain experts autonomy to assess the quality (interpret the outcomes and validate KPIs) of AI models in production and facilitates the ability to promote or demote AI models for inferencing without a full dependency on data scientists or ML engineers.”
Gartner, “A Guidance Framework for Operationalizing Machine Learning” Soyeb Barot, 14 May 2020
ModelOps platforms offer the following advantages for AI operations:
Accelerated delivery of AI products to business users
Better alignment between business domain experts, data science and engineering
Constant feedback on modeling outputs by business and/or operational experts
Governance and quality assurance of models and modeling outputs in conjunction with business domain experts
Gartner “Assessing DevOps in Artificial Intelligence Initiatives”, Carlton Sapp, 21 February 2020
In-depth resources to help you scale and govern your Enterprise AI initiatives
ModelOps Masterclass Series
ModelOps Essentials Guide
ModelOps includes MLOps
This paper explains what models are and details the core requirements for ModelOps, which is the technical and organizational capability essential to successfully deploy, manage and govern models at scale in large, complex enterprises.
Many large enterprises struggle to scale AI. Why?
Number of Models
Each business will need to manage hundreds of models to account for business process variations, personalization, and unique customer segments.
The rapid and ongoing innovation in the data & analytics space leads to complexity unmanageable for even the most expert IT teams.
Adhering to strict and ever-increasing model regulatory requirements becomes more difficult as the use of AI expands across industries.
Ineffective collaboration across teams that need to work well together can make scaling difficult or impossible.
ModelOps Explained in 2 minutes
ModelOps is the systems and processes that automate the deployment, monitoring, governance, and continuous improvement of data science models running 24×7 within the enterprise’s most critical business processes and applications.
How do industry leaders drive AI at scale?
Enterprises drive AI into core processes at scale by focusing on three areas:
Leaders understand that deploying models into mission-critical applications requires them to run 24×7, without fail, with the same operational controls, tooling, and automation that support other technologies.
Leaders fully automate ModelOps processes, from deployment through monitoring and governance, to effectively manage a model within business SLAs and eliminate manual processing, reducing risk, cost, and model time to business.
ModelOp Solutions: built to help large enterprises address these challenges.
Designed to meet the needs of data scientists, IT, and business leaders. Backed by our proprietary software, ModelOp Center, and our expertise in data science, data engineering, infrastructure, software, and business transformation.
Align leadership with a ModelOps Assessment of your current state against best practices.
Speed to Value
Put 1-2 priority AI Models in Business, while designing foundation ModelOps capabilities. Delivered on ModelOp Center.
Deploy an Industrialized ModelOps program across the enterprise, customized to the specific requirements of the business. Delivered on ModelOp Center.
ModelOp Academy provides workshops, education and training to build awareness and develop core ModelOps skills and capabilities.
Any reference made to “ModelOps” is about model operations platforms, not the company ModelOp. Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s Research & Advisory organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.