In the field of data science, the deployment and operation of AI/ML models can be a challenging task due to various reasons, like increasing the amount of data. To overcome these challenges, the concept of ModelOps was introduced in the early 2020s. ModelOps encompasses a set of practices and processes that not only aid in the creation of models but also in the deployment of them in a scalable and flexible manner. This focus on ModelOps has become increasingly important as organizations strive to effectively utilize machine learning models in their operations. ModelOps has become a rapidly growing field as a result. So let’s take an introductory dive into the subject and understand what ModelOps is and how it is becoming the point of attraction for AI and ML developers.
What is ModelOps?
ModelOps can be referred to as the management and operationalisation of ML models within the ML processing of any organisation. As many big organisations are using a load of Ai use cases, it becomes a compulsion to develop these use cases so that they can have higher speed and scalability and improved quality and accuracy. Like DevOps, MLOps and DataOps, ModelOps is also a set of practices that involves the enhancement of a wide range of activities, such as machine learning model development, testing, deployment, monitoring, and maintenance.
According to Gartner,” ModelOps (or AI model operationalisation) is focused primarily on the governance and life cycle management of a wide range of operationalised artificial intelligence (AI) and decision models, including machine learning, knowledge graphs, rules, optimisation, linguistic and agent-based models. Core capabilities include continuous integration/continuous delivery (CI/CD) integration, model development environments, champion-challenger testing, model versioning, model store and rollback.”
The aim behind applying ModelOps in the development process is to ensure that ML models are better developed and used efficiently in real-world applications. This helps organisations to enhance their capability to generate business value from their ML models and investments. ModelOps is a relatively newer term in the technology space, but it is becoming way more important because organisations are relying more on machine learning models for critical business processes.
Talking about the challenge that ModelOps helps organisations overcome, mainly in the deployment of machine learning models, such as difficulties in the reproduction of final results, less transparency, slow iteration times etc., the ultimate goal of applying ModelOps is to maximise the value of organisations investment on machine learning.
Component of ModelOps
The components of ModelOps can be segregated into the following groups:
Model development: this section may include the tools for extracting prepared and feature-engineered data from data sources and for model selection and training.
Model deployment: this section contains tools for model packaging, model and architecture provisioning, and model deployment to the production environment.
Model Monitoring: this section includes tools for monitoring and capturing model performance and data and model drift while interfacing for model retraining.
Model maintenance: this section includes tools for managing models, storing model versions, model documentation and requirement and model retraining place.
Model governance: this section includes a set of rules to control access, implement policies and track activity for models and their results.
Collaboration and communication: these are a set of rules and management skills to make better collaboration between data scientists and DevOps teams, communication between stakeholders, and integration with other tools and systems.
Benefits of ModelOps
However, the term ModelOps is inspired by the concept of DevOps and MLOps, but its adoption ensures:
- Developers are provided with CI/CD(continuous integration / continuous delivery)
- Improved Development Environment
- Better Testing
- Controlled model versioning
One can think of ModelOps as a part of MLops, which mainly focuses on keeping deployed models ready to involve in continuous model developing or retraining and synchronized deployment. There are several benefits of ModelOps, such as:
- Improved model quality: ModelOps helps ensure that machine learning models are reliable, accurate, and consistent by introducing processes for testing, validation, and monitoring the performance of the models.
- Faster model deployment: ModelOps automates the deployment process, reducing the time it takes to get models into production and increasing the speed at which new models can be deployed.
- Better model governance: ModelOps provides a framework for managing the lifecycle of machine learning models, including versioning, auditing, and regulatory compliance.
- Increased agility: ModelOps enables organizations to respond quickly to changes in business requirements or market conditions by allowing teams to update or replace models in a timely manner.
- Improved operational efficiency: ModelOps streamlines the operations of machine learning models, reducing manual effort and increasing the scalability and reliability of the models.
Difference between ModelOps and MLOps
Often organisation uses the terms ModelOps and MlOps interchangeably, but when going into the deeper section, we find subtle differences between the two.
In one of our articles, we explored the MLOps profoundly and learned that it is a broader term that helps in processing the entire lifecycle of AI/ML models, from processing data to deployment and management of the models. This term consists of an entire set of practices for data processing, data management building, testing, deploying and monitoring AI/ML models.
On the other hand, ModelOps only focuses on the operational aspects of AI/ML models. ModelOps includes practices for processes such as model management, deployment, monitoring, and maintenance. Involving these practices in development ensures that the models are deployed, monitored, and maintained efficiently in production.
In conclusion, we can say that ModelOps practices are more focused on operational aspects of AI/ML models, while MLOps is a broader term that includes ModelOps as a subset that majorly focuses on the deployment, maintaining and monitoring the AI/ML models.
Is MLOps a combination of DataOps and ModelOps?
In the above section, we learned that ModelOps is a subset of MLOps that focuses only on operational aspects of AI/ML models, but due to this article, we have learned that DataOps is a set of practices and technologies that are designed to address the challenges of managing and processing large volumes of data in a fast-paced and constantly changing environment.
So we can say that the terms MLOps, ModelOps and DataOps are related but distinct concepts that are all part of the broader term of data science and machine learning.
The focus point of DataOps is to ensure that data is high-quality, accessible, and secure and that the process of moving data through the pipeline is efficient and reliable.
On the other hand, ModelOps is a set of practices, tools and technologies focusing on streamlining the model development and deployment process, making it more efficient and reliable while also ensuring that models are continuously monitored and updated to reflect changing data and business needs.
Here we can say that MLOps is a combination of DataOps and ModelOps that is focused specifically on the machine learning workflow, from data collection to model deployment. While DataOps and ModelOps are both important components of MlOps, they are not the same. Rather MLOps is an umbrella term that encompasses both DataOps and ModelOps, as well as other practices and technologies that are specific to the machine learning workflow.
Implementing a comprehensive data-driven strategy that involves the use of DataOps, ModelOps, and MLOps can be a complex and challenging task for organizations. This is because it often requires the collaboration of different teams with different areas of expertise and can involve the integration of a variety of technologies and processes.
UnifyAI, DSW’s platform, is an ideal solution to overcome the challenges and intricacies that organizations face when implementing DataOps, ModelOps, and MLOps simultaneously. Collaboration and communication among different teams are necessary to address the complexities. UnifyAI integrates MLOps, DataOps, and ModelOps to create a culture of collaboration and knowledge-sharing among teams. This allows organizations to achieve a comprehensive data-driven strategy and reduces the dependence on a large number of teams and members by offering all necessary technologies and tools aligned with best practices for implementing smooth ML workflows.
About DSW
DSW, specializing in Artificial Intelligence and Data Science, provides platforms and solutions for leveraging data through AI and advanced analytics. With offices located in Mumbai, India, and Dublin, Ireland, the company serves a broad range of customers across the globe.
Our mission is to democratize AI and Data Science, empowering customers with informed decision-making. Through fostering the AI ecosystem with data-driven, open-source technology solutions, we aim to benefit businesses, customers, and stakeholders and make AI available for everyone.
Our flagship platform ‘UnifyAI’ aims to streamline the data engineering process, provide a unified pipeline, and integrate AI capabilities to support businesses in transitioning from experimentation to full-scale production, ultimately enhancing operational efficiency and driving growth.