ModelOps and the Deployment Process


ModelOps and the Deployment Process Team
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Automation is a massive part of building AI models. Without it, ModelOps would be a time-consuming process that took a lot of manpower to finish. One of the best parts of ModelOps is how things are moving forward using the CI CD process.

This is essentially the same process used in software engineering to deliver exceptional results. It makes the production deployment of models easy, and you get plenty of other auxiliary results. By using the software engineering DevOps model, we now have boundless possibilities for people in the machine learning industry.

Containers Are Now Key

AI models are notoriously difficult to get into production. However, when you do eventually get a model that is ready to be put into production, you need a way to do it correctly. How should the production deployment process work? The answer is to use something that comes from continuous integration and continuous deployment.

You can think of containers as lightweight virtual machines that share the same kernel. They are even more remarkable than virtual machines because you can interact with them as if they were APIs. This has been one of the major innovations in the software deployment industry, and it has led to many benefits for ModelOps.

Containers are great because you can package everything needed to successfully deploy your model in one simple container. That container can then be replicated, and you can even swap things out when improving your model. This makes the process easier, and you can be more agile with your CI CD process.

What Is an AI Model?

Having standard processes for deploying AI models is also a crucial component of this new paradigm. With standardization, you can then create containers and software tools that streamline the entire process.

Your data scientists no longer need to be experts in software engineering and DevOps. They can focus on making them models as good as possible, further improving the results you get from your projects. It is one of the best reasons why it is crucial for you to have a standard process for how you deploy and work with models. Leveraging the CI CD process that you find in software engineering can be a crucial step.

Using CI for Automating the Build Process

The CI CD process is exceptional in software engineering. You can easily take source code and turn it into a running application quite easily, and it is automated. This process is now more pronounced in developing AI models. There are now many tools available to help automate this process for model development. Because of this, ModelOps is becoming easier for data scientists to do.

They no longer need to invest the time and effort needed to put models into production. When you combine those software tools with containers, you get a streamlined process for turning models into production applications. It enables your teams to deliver results quickly and effectively. It also means you can spend the majority of for time building the model, which is currently not what is happening in most machine learning projects.

About the Author Team
Enterprise AI/ML Application Lifecycle Management Platform

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