What Is ModelOps? ModelOps vs. MLOps

As organizations push to leverage AI solutions in their operations and decision-making processes, a few problems have emerged, particularly around implementing the solutions across numerous use cases. This has necessitated the need for robust tools and practices that can help in the deployment and management of machine learning operations. That’s where ModelOps and MLOps come in. With these systems, data scientists within an organization can effectively streamline operations and increase the efficiency of machine learning models.

But what exactly are these tools, and how could they benefit your business? This article will explore ModelOps and MLOps in their entirety, including what modelOps is, how it differs from MLOps, and what it can do for your business.

What is ModelOps?

ModelOps, short for Operations for Machine learning models, is a set of tools, processes, and practices primarily geared towards deploying, monitoring, and managing various operationalized AI and decision models, including knowledge graphs and ML models throughout their lifecycle. [1]

At its core, ModelOps aims to improve the accuracy and efficiency of model-building processes while ensuring proper deployment and maintenance of ML models.

Unlike traditional software models, ML models require constant monitoring and maintenance to produce consistently accurate results. ModelOps aims to streamline these requirements by providing the framework required to manage ML models throughout their lifecycle, from development deployment, and maintenance.

ModelOps achieves this by providing a collaboratory framework for data scientists and operational experts to enable them to manage the production and maintenance of ML models throughout their lifecycle. In a way, it brings together the creative and operational requirements of machine learning systems.

Components of ModelOps

ModelOps is comprised of various components that work together to ensure the proper development, management, and deployment of ML models so that they work efficiently and effectively. These components include:

Model development

Model development is the process of creating and training ML models. It is the first component of ModelOps and is typically led by data scientists and domain experts who work together to identify the most suitable data sources to fulfill the relevant features of a machine learning model.

The process typically includes tasks like data preprocessing, feature engineering, model selection, and hyperparameter tuning. [2] Essentially, these processes help IT professionals prepare data for use in the model, select and transform the features in the data to improve the model’s accuracy, and choose an appropriate algorithm and architecture for the model based on the data and problem at hand.

Model versioning

Model versioning is the management of different versions of a machine learning model. This process is crucial for efficiently tracking and managing changes to ML models and their dependencies. It can also track the performance of different models to determine which models work well and which ones need improvement.

Model deployment

Model deployment is the process of deploying ML models to production systems. The process involves everything from the containerization, orchestration, and automation of deployment pipelines.

Here, data scientists ‘package’ the model and its dependencies into a container to make it easier to deploy, manage the deployment of different ‘containers’ across various servers and clusters, and automate the deployment process. This reduces the time and effort required to deploy an ML model.

Model monitoring

Model monitoring is the process of continuously monitoring the performance, accuracy, and drift of deployed models. Developers typically collect and store all data pertaining to the model’s performance, set up automated alerts to alert them when the model performs below expected levels, and set up automated retraining processes to return the model to its expected performance threshold.

ModelOps processes

ModelOps follows a specific sequence of operational processes to achieve its goals. These processes include:

ModelOps process, infographic

Build

This is the first phase of ModelOps. Here, developers collect the data necessary to solve a specific business problem, then refine it so that it can be used to train ML models.

Manage

In this phase, the collected data is used to train models, then the management processes begin. The management processes include testing, versioning, and approval. All management processes are carried out from a central repository.

Deploy

Once the model has been tested and versioned in the management phase, it is now ready for deployment. Model deployment is typically done through a pipeline similar to the development environment and is meant to integrate the model into the business.

Monitor

Even after successful deployment, there’s still room for error. Therefore, deployed models are continuously monitored to ensure accuracy and consistency. By continuously monitoring the models, you’re able to quickly detect any anomalies and prepare the model for retraining.

What is MLOps?

Before we get to a head-to-head comparison, you first need to understand what MLOps is. MLOPs, short for Machine Learning Operations, is an integral part of the machine learning model engineering process. It focuses on streamlining the production, maintenance, and monitoring of ML models. [3]

MLOps achieve these core processes through various tools and processes, including:

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Data management

Machine learning models need accurate and reliable data to perform effectively. Therefore, all datasets must undergo various data management processes, including data cleaning, preprocessing, labeling, and version control.

Model development

The ideal machine-learning model should be accurate, reliable, and scalable. To achieve this, developers need to choose appropriate algorithms, tune hyperparameters, and test the model’s performance.

Model deployment

Once the model has been developed and tested, it needs to be deployed in a production environment. The deployment process typically involves creating a production-scale version of the model, containerizing it for easy deployment, and integrating it into existing systems.

Continuous integration and deployment (CI/CD)

To make the building, testing, and deployment process more streamlined, MLOPs need to automate these processes in order to ensure that all changes to the model or code are automatically tested and validated before the model is deployed into production. [4]

Monitoring and feedback

After deployment in the production environment, developers need to continuously monitor the model’s performance. This typically involves collecting data on any anomalies and errors, then providing feedback to improve the model.

Model governance

MLOps has governance principles that ensure that ML models are used responsibly, ethically, and securely. This typically involves continuously monitoring the model to remove bias and ensure fairness, privacy, and security.

ModelOps vs. MLOps: Differences

ModelOps and MLOps are often used interchangeably. This comes as no surprise considering how related they are, particularly around ML model development, deployment, and maintenance. With that said, the two principles have more differences than similarities. At their core, they are two different entities that serve different purposes.

Here are some of the most notable differences between the two:

Focus

MLOps is basically the DevOps of ML algorithms. It focuses primarily on integrating DevOps principles into machine learning workflows, thus enabling agile and efficient development and deployment of machine learning models in production pipelines.

ModelOps, on the other hand, focuses on the governance and management of machine learning models throughout their lifecycle. This ensures that they perform as expected and deliver accurate predictions.

Approach

MLOps is heavily inspired by the working principles of DevOps. It provides a holistic approach to streamlining the practices and processes of software change and updates by combining people, processes, and technology to manage the machine learning pipeline.

ModelOps, on the other hand, take a more specialized approach by streamlining the operation of ML and other AI models in order to make them more effective and efficient through various tools and techniques, including version control, deployment pipelines, and monitoring.

Applications

MLOps is primarily used for developing ML models. It encompasses everything from the actual source code of ML models to the testing, training, validation, and retraining of ML models. To achieve this, various players, including data scientists and other IT professionals, collaborate to identify the proper data sets for use in the model, develop the model, and ensure that it is aligned with business objectives.

ModelOps, on the other hand, is a combination of platforms and technologies used to ensure reliable, efficient, and optimum outcomes for ML models in production pipelines. This includes everything from inventorying all models in production to ensuring model accuracy, compliance, and risk management and control.

Benefits of ModelOps

At its core, ModelOps is primarily geared toward helping organizations manage the lifecycles of their ML models. This presents several key benefits, including:

Improved efficiency and collaboration

One of the biggest problems facing organizations when it comes to managing their ML models is a lack of collaboration between the teams deploying the models and the teams overseeing the models’ operations.

ModelOps can help close this collaboration gap by setting transparent goals for each model, outlining clear processes for all teams involved, and assigning definitive responsibilities for all teams involved in the project.

Visibility and insights

Most ModelOps tools on the market feature clearly outlined interactive dashboards and key metrics that enable organizations to easily monitor and evaluate the performance of their ML models.

This provides added transparency into how teams deploy and utilize the organization’s models and introduces better explainability into AI-enabled business outcomes. ModelOps achieves this by presenting information on ML models in a way that can be easily understood, even by non-technical business executives. [5]

Reduced costs

Using ModelOps platforms can significantly reduce the time, effort, and resources required to deploy, monitor and manage models at every stage of the lifecycle. It reduces the time it takes to put models into production and automates all corresponding workflows, thus reducing time and effort across the board. This, coupled with the improved collaboration between teams, significantly improves efficiency and the ability to control infrastructure costs.

Increased revenue

ModelOps streamlines your entire production pipeline throughout a model’s lifecycle, thus ensuring that your analytical investment delivers value to your business. It does this by getting the model out of the lab and into real-world applications faster and more effectively. This way, organizations are able to realize the value of their investments faster and boost ROI.

Better risk management

Putting an ML model into production is a costly affair, especially if you’re deploying several models at the same time. This necessitates the need for an effective model governance and risk management platform – that’s where ModelOps comes in. By enabling you to monitor your models in real time, you get to realize any potential risks before they cause real damage.

ModelOPs also increases transparency in an organization’s AI assets, thus reducing the risks associated with ‘shadow IT solutions that are built or utilized without explicit departmental approval.

Improved scalability

ModelOps gives you the ability to deploy models anywhere, be it on the cloud, in applications, or on edge. It also helps in model governance and maintenance, thus improving the model’s performance. Additionally, by optimizing the production lifecycle, ModelOps ensures that models are deployed as lightweight as possible, thus promoting scalability.

ModelOps best practices

Some of the most notable best practices for ModelOps include:

• Model versioning: It involves keeping track of different versions of the model to ensure that they can be easily deployed, maintained, and updated. Ultimately, this helps to manage dependencies, track performance changes and avoid the deployment of inefficient or ineffective models.
• Monitoring: Continuously monitoring deployed models can help identify potential issues that may affect the performance and accuracy of the model.
• Automated testing: Automating testing and quality assurance procedures ensures models work correctly and accurately and reduces the likelihood of errors.
• Collaboration: Collaboration in ModelOps typically involves ensuring that all teams within an organization work together and communicate effectively to deploy and maintain models in production.
• Model explainability: Transparency and accountability are vital for compliant, ethical, and effective ML model utilization. It can be achieved through feature importance analysis, sensitivity analysis, and visualization.

Wrapping up

Both ModelOps and MLOPs are vital for the proper development, deployment, and management of machine learning and other AI models – you can’t have one without the other.

They are crucial elements of the machine learning lifecycle and ensure that models are deployed efficiently, accurately, and securely. And, despite their apparent differences, ModelOps and MLOps present innumerable benefits for development teams and the organization as a whole. Discover our MLOps consulting services.

References

[1] Gartner. com. Modelops. URL: https://www.gartner.com/en/information-technology/glossary/modelops. Accessed April 14, 2023
[2] Datacamp.com. Parameter Optimization Machine Learning Models. URL: https://www.datacamp.com/tutorial/parameter-optimization-machine-learning-models. Accessed April 14, 2023
[3] Ibm.com. MLOps. URL: https://www.ibm.com/products/cloud-pak-for-data/data-science-mlops. Accessed April 14, 2023
[4] Github.io. MLOPS/CICDML. URL:  https://mlops-guide.github.io/MLOps/CICDML/. Accessed April 14, 2023
[5] Towardsdatascience.com. Scale and Govern AI Initiatives with ModelOPs. URL: https://towardsdatascience.com/scale-and-govern-ai-initiatives-with-modelops-afdc33ce1171?gi=7d8fada5f7ae. Accessed April 14, 2023