What is ModelOps?

in MLOps

What is ModelOps?

In concise words, ModelOps is a practice that introduces a modern set of tools and techniques that makes the process of developing, deploying, and managing predictive analytics models, including artificial intelligence and machine learning, more effective.

    You’ve heard of DevOps, AIOps, MLOps, DevSecOps, and many other derivatives of the overused “Ops” suffix. So you’re wondering, is this ModelOps one of the many “Ops” terms?

    ModelOps is a crucial term, technology and practice, branched from DevOps, that happens to share the infamous Ops suffix because of its implementation in Operations. We’ve explained what exactly it is, how it came about, and its goals in this article.

    A survey by Gartner in 2018 recorded that 37% of the participants have deployed AI solutions in some form. It is gathered from this survey that implementing AI at an enterprise scale is still a nightmare, specifying difficulty in deployment as the major obstacle. As a result of this problem, enterprises can only deploy AI solutions on small scales, or in most cases, be unable to deploy their models at all. In similar research, Forrester also reported that Data Scientists complain that their enterprise machine learning models are usually never deployed.

    Creating and deploying machine learning models individually, Data scientists face many technical and business challenges. On top of that, it takes too long to deploy a single model successfully.

    Because of the scale at which machine learning solutions are created and used in enterprise applications, there’s a need to address these underlying issues. There should be a straightforward process to deploy, monitor, operationalize and govern these models for use at an enterprise scale. This need is what brought about the term, ModelOps.

    So, What Exactly is ModelOps?

    Operations for Machine learning models, ModelOps, as defined by Gartner, is a practice that is “primarily focused on the governance and life cycle management of a wide range of operationalized artificial intelligence (AI) and decision models, including machine learning, knowledge graphs, rules, optimization, linguistic and agent-based models.”

    ModelOps is a structured approach to operationalizing all predictive analytics and machine learning and artificial intelligence models -- it a framework that allows Data scientists to monitor and govern data science models while ensuring scalability of the models.

    ModelOps evolves around the entire lifecycle, providing a CI/CD integration, model testing, versioning, and ensuring a continuous operational cycle.

    Process of ModelOps

    Image credit https://commons.wikimedia.org/w/index.php?curid=99598780: Image credit https://commons.wikimedia.org/w/index.php?curid=99598780:

    Like in DevOps, ModelOps has some operational process it follows to achieve its goals.

    • Build

    The build phase of ModelOps starts with collecting data necessary to design a solution for a business problem. After this, the data will be refined and prepared to train algorithmic models built in the same phase.

    • Manage

    In the management phase, the model is trained with available data then management processes such as testing, versioning, and approval are done from a central repository.

    • Deploy

    After testing and versioning the model in the management phase, the solution is deployed into production using a pipeline similar to the development environment.

    • Monitor

    After a successful deployment, the model is continually monitored to ensure the effectiveness and accuracy of its prediction. When the underlying data drifts and is no longer accurate, monitoring helps quickly recognize it and set it up for retraining.

    How ModelOps Solves Models Lifecycle Problems

    ModelOps effects and solves the operational problems of enterprise ML and AI models in many ways. This includes;

    • Model Deployment: Traditionally, data scientists have to build and deploy models into production manually using various toolsets that take time and render the process vulnerable and less efficient. ModelOps provide tools that remove the need for manual model deployment to both cloud and on-prem infrastructure while ensuring the scalability and security of the model.
    • Degradation: One property of AI/Ml models is that they degrade and drift off over time. To ensure that models keep giving desired predictive analysis, they need to be continuously trained and retrained with data. ModelOps solve this problem by bringing intuitive monitoring into play. It allows you to monitor your machine learning models to quickly know when it is degrading and needs to be retrained.
    • Improves ML/AI Models Business Input: By allowing enterprise-scale deployment of models, ModelOps improves the business impact of models and enables you to link model performance to business key performance indicators (KPIs).

    Benefits of ModelOps in Enterprise ML/AI

    1. Innovation: ModelOps avails data scientists and model engineers free time to develop innovative solutions to improve business and customer experience.
    2. Role Designation For Improved Collaboration: ModelOps assigns definitive roles to each professional, making DevOps and Software engineering teams less involved in model packaging and fostering productive collaboration between experts in each field.
    3. Reproducibility: By implementing ModelOps, Operations teams do not need to create a new and unique deployment environment for every model. It enables the use, keep, and control of the same data pipeline configuration across different models. This ensures that infrastructure and resource provision is used optimally.
    4. Continuous Monitoring: ModelOps enables data scientists to continuously monitor their model to ensure the data quality, model accuracy, and performance required in enterprise models.
    5. Continuous Improvement: Continuous monitoring leads to constant improvement by regularly assessing, retraining, and modifying the enterprise models.
    6. Automation: ModelOps automates time-taking model workflows and provides visibility into each step. Processes such as model review, testing, and approvals are all automated to make the workflow faster and more efficient.

    How Can I implement ModelOps?

    ModelOps, like MLOps or DevSecOps, can be implemented in your predictive analysis workflow through major ModelOps solutions developed by reputable cloud Ops companies.

    A front name in providing ModelOps solutions is ModelOp, a company that offers predictive analytics solutions for scalable enterprise machine learning and artificial intelligence models and applications.


    In the CIO Summit Keynote event in October 2020, the co-founder and Chief Enterprise architect at ModelOp explained how ModelOps approaches putting enterprise AI models into operation while providing governance, risk management, and compliance.

    Other major companies that provide comprehensive ModelOps solutions include, Algorithmia, QuickPath, IBM, and Cloudera.

    ModelOps can be used across various industries that use machine learning models and predictive analysis algorithms to provide business solutions. The use cases cuts across fintech, health tech, stock, and forex predictions, and other industries using disruptive technology to revolutionize business.

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