ModelOps and MLOps: Do You Understand the Difference?
The majority of machine learning projects have a long lifecycle. That means the decisions you make at the start about what MLOps tools to use will have a massive impact on how easy projects are to manage down the line. It is why you need to do a lot of research at the start to identify the right mix of tools, and you also need to identify the software and infrastructure you will choose. These will help you prevent being locked into any one vendor for your project.
ModelOps is the final missing piece of the puzzle that organizations need to have an integrated model development strategy that gets results. Machine learning models aren’t the only ones that can be deployed into production, and ModelOps is a superset of MLOps. That is to say; it contains everything that MLOps does and more. ModelOps will help your organization get more out of your investment into artificial intelligence and machine learning.
Comparing ModelOps and MLOps
The major difference between ModelOps and MLOps is that ModelOps focuses on all AI models. MLOps is mostly focused on machine learning models. ModelOps also provides the dashboard, reporting, and other information needed by business leaders to understand what is going on with the project. It enables teams to get a birds-eye view of how things are working, which is a crucial step in ensuring that projects lead to suitable outcomes for the corporation.
ModelOps helps to shine a light on what is going on across the enterprise. It monitors the performance of models and also allows for the retraining of those models as well. It makes the deployment of ML models easier. It gives teams the ability to manage infrastructure, as they can now plan and track what will be needed in the future. It will also help with scaling machine learning models, which can be a big problem.
Why ModelOps Matters
ModelOps make scaling AI pipelines a lot easier. The information it provides allows enterprises to be more effective in deploying models. It also connects those models back to business goals, which ultimately leads to growth in the business. Many people think of it as the connective tissue that ties other parts of the machine learning operations pipeline together. It also allows organizations to focus on reusing and improving models instead of starting over from scratch every time.
How It Fits Into Your Tech Stack
The main thing that ModelOps adds to your AI operations is trust. The business leaders inside your company will now have access to the data that explains how AI is being used inside the company. That transparency will help them see what ModelOps contributes to their bottom line. One of the eventualities of this reality is that these executives will now understand why machine learning and artificial intelligence are so crucial to the business. They will want to do things that help them adopt even more technologies inside their organizations. The end result of all of this is the long-term success and sustainability of your AI and ML models.