Why AI ModelOps is important + How to implement it with AI software
As artificial intelligence is widely applied, businesses build various products or services based on AI or make predictions for decision-making. However, not all models move from the testing and validation phase to the deployment phase. If one model is deployed, you still have to retrain or revise one model as new data comes in and the business requirements change. All models will be outdated someday. Regular maintenance and updates are needed. For businesses that want to thrive with AI, it is critical to manage AI models throughout all model life cycle phases to ensure they fit different business requirements and reach the deployment phase. That is why we need ModelOps.
｜Who need this blog｜
- You lead an AI team. You want to better manage the models of your team, optimize the workflow of building an AI project.
- You are a product manager, and you need to monitor the status of every AI model on your product/service.
- You are a data scientist or an AI engineer, and you are looking for some tools to better monitor the training and deployment status for your models.
｜This blog addresses the following questions｜
- What is ModelOps?
- What are the differences between ModelOps vs MLOps?
- Why is ModelOps important? Value of ModelOps
- What are the critical features of ModelOps?
- AI software and development tools for building ModelOps
What is ModelOps?
ModelOps is a capability that focuses on getting models into 24/7 production. - Forbes
ModelOps refers to the model lifecycle management and model governance for your organization. The concept was proposed by two IBM AI researchers in 2018. Gartner defines it as "the governance and life cycle management of a wide range of operationalized artificial intelligence and decision models." In a Forbes interview, the Co-Founder and Chief AI Architect of ModelOp explained it as "the capability that focuses on getting models into 24/7 production." That is, ModelOps is all about monitoring all AI models in your firm, keeping your models updated, ensuring your models create values in an automated way.
ModelOps vs MLOps: what are the differences?
The main difference between ModelOps and MLOps is the goal. ModelOps focuses on solving the enterprise-level operational issues of model building and management, while MLOps focuses on automating the model iteration and experimentation, and the availability of CI/CD. ModelOp, the leading firm that provides ModelOps solutions, gives an explicit explanation: "MLOps tools are designed to address the needs of the data scientist and model development and refinement in the experimentation phase in the lab. ModelOps is about addressing the needs of the enterprise and managing the operations and governance of all models in production." (Learn more about the differences between MLOps and ModelOps)
Why is ModelOps important? Values of ModelOps
Figure.1 Benefits of AI ModelOps
With ModelOps, your organizations and firms can increase operational efficiency as models are monitored and updated regularly. Statistics and research show that without good model governance, there will be risks for your organization's AI development:
- Low model deployment rate.
Based on SAS's survey, only about 50% of models are ever put in production, and those models take at least three months to be ready for deployment. One reason is that businesses need changes, and models are outdated.
- Process Management Debt.
A team of AI experts at Google explores the "Hidden Technical Debt in Machine Learning Systems". They revealed that as there are hundreds of models running simultaneously in a mature ML system, managing and allocating resources among models effectively and how to conduct CI/CD safely becomes a problem.
If you systematically monitor and update models, benefits include:
- More real-time insights for decision making.
Delivering insights on time is vital for business. After automating the model deployment process, real-time insights are thus accessible. This is extremely helpful for certain applications, like monitoring a diabetic's blood sugar levels or examining a patient's atypical respiratory signals.
- Higher capability of Model A/B Testing.
By using ModelOps platforms, you are able to deploy multiple models simultaneously for A/B testing. A/B testing allows you to evaluate the effectiveness of a model in real-world scenarios. In many cases, such as e-commerce, evaluating your model with those traditional indexes isn’t sufficient, you need to A/B test models before making the decision of updating models. For instance, with ModelOps software, you can easily test different AI models to identify which model brings a better conversion rate.
- Transparent model governance.
ModelOps platforms usually provide a dashboard for you to view all the models across different projects. Transparency of the model's status allows you to review the performance of every model, knowing when to update it.
- Economical resources allocation.
Based on the transparency of model status, you can now evaluate the effectiveness of each model. As a result, you can allocate resources such as computing, maintenance, etc., more economically.
What are the critical features of ModelOps?
If you plan to build ModelOps and operationalized AI, there are three key elements:
- Automated and standardized workflow among different teams
- AutoML allows non-technical staff help to identify their needs and ensure data quality
- Dashboards for visualizing the status of all models
AI software and development tools for building ModelOps
To get started with ModelOps, you might need some AI softwares or platforms that provide an easy way to create dashboards for your models. Here are some AI software tools for ModelOps:
ModelOps is one of the leading ModelOp’s platforms that offers end-to-end governance of model-driven initiatives.
: Datatron’s Model Catalog provides the model transparency and lineage throughout the AI model’s operationalization, monitoring, and governance lifecycle. When models are first registered, Datatron’s Model Catalog will automatically capture the properties, metadata, and more and populate the detail throughout the system. Users are able to keep track of their models across different teams.
3. IBM Watson
: IBM Watson supports ModelOps from data collection, model building to model deployment. IBM’s Cloud Pak for Data allows users to view all the models on one single platform.