ModelOps vs MLOps | The Comprehensive Guide

Introduction to MLOps and ModelOps

As technology advances, Generative AI and Machine Learning drive disruptive cases across organizations, from small business predictive maintenance to large enterprises facing supply chain concerns. These advancements assist an organization in cutting down the time taken during manual operations. 

While navigating this change, two terms are generally used interchangeably—MLOps (Machine Learning Operations) and ModelOps (Model Operations). However, they are used for different purposes and benefit enterprises. In this blog, We will focus on distinguishing between ModelOps and MLOps, the perks of each, and how different organizations can use the two models to improve performance.

Primary Users of MLOps and ModelOps

ModelOps

ModelOps Users include Business Decision-Makers, who align AI models with strategic goals; Data Scientists, who deploy predictive models without infrastructure concerns; and Compliance and Risk Teams, who ensure regulatory adherence

MLOps

MLOps Users consist of Data Engineers, managing data pipelines for smooth training data flow; ML Engineers, automating the machine learning lifecycle; and IT Operations Teams, overseeing the deployment and monitoring of ML systems

What is MLOps?

It is a process of managing the workflows of ML models. It's a subset of ModelOps. It is the practice of building, evaluating, deploying, and maintaining ML models. It seeks to unify ML workflow to standardize and streamline the machine learning life cycle. Nowadays, ML engineers have to manage their workflows in production.

Activities that are performed as part of MLOps

Following are some activities :

• Model training/retraining

• Integration with data pipeline and model deployment

• Integrate ML models into productions

• Automate the machine learning model's lifecycle management

• Monitoring the performance of the model in production

• Updating the models when needed

What are ModelOps?

It enables organizations to implement and scale AI solutions while effectively managing and monitoring their performance. Integrating DevOps, DataOps, and ITOps provides the necessary processes, tools, and technologies for deploying, monitoring, and governing machine learning models. This approach automates repetitive tasks, allowing teams to focus on more critical issues.

As an extension of MLOps, it incorporates additional skills related to IT operations, risk management, and governance, which are essential for unlocking enterprise value with AI. The AI pipeline includes data management, data wrangling, model training, deployment, and business applications, serving as a connective framework that enhances collaboration. By offering a shared tool for tracking AI assets, organizations can reduce risk, optimize resource allocation, and improve model reuse.

Activities that are performed as part of ModelOps:

Following are some activities:

• Making ML and AI workflow operationalize

• Automate the operations for AI solutions

• Automate all the processes, including model training pipeline, version control, data management, experiment tracking, testing, and deployment.

How is ModelOps different from MlOps? 

The newly emerged technique that includes people, processes, and technology that give an edge to swiftly and safely optimize and deploy machine learning models. Click to explore about our, MLOps Platform - Productionizing ML Models

MLOps vs. ModelOps: Choosing the Right Technology

It is important to know when to use MLOps or ModelOps because they are two different technologies with different business goals.

When to Choose MLOps

• Focus on Machine Learning Models: If your priority is to focus on the operation of the machine learning models, then it is preferable to use MLOps, as this is the approach created for this purpose.
• Need for Technical Integration: If it is essential to integrate with data science platforms and technical environments, then it offers the right fundamental structures with MLOps.
• Data Science-Centric Team: However, if most of your team members are data scientists and MLOps engineers, getting used to it is easy as it will suit their skill sets.

When to Choose ModelOps

• Diverse Model Types: If you use multiple models, such as ML, statistical, predictive, and rule-based, ModelOps provides an overarching solution.

• Regulatory and Compliance Requirements: The decision to go for ModelOps should be made where strict compliance with industry standards and or regulatory requirements is necessary because it affords strong levels of control.

• Enterprise-Wide Collaboration: Whenever your business processes require close integration of data science, IT, and other lines of business, ModelOps ensures this integration is seamless.