How Containers Simplify the MLOps Model Production Pipeline


How Containers Simplify the MLOps Model Production Pipeline Team
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Container technology has been revolutionary in software development and engineering. In terms of machine learning models, containers and CI/CD have also been instrumental. Containers are relatively new, but they offer tremendous benefits in model development, model validation, and the model deployment lifecycle.

A container is a lightweight virtual machine that packages everything you need into one. That makes it the perfect option for people who like flexibility. By using containers, you don’t need to reinvent the wheel when developing. Containers make it possible to do collaborative development without any configuration issues that come from using different computers. It also makes the field of MLOps a lot better for developing machine learning models. Tools like Kubernetes have been instrumental in changing how data scientists work.

Simplify Your Development

Container technology has been instrumental in simplifying the development cycle to deploy models. The reason that containers work so well is that they allow machine learning engineers and data scientists to focus more on creating the model instead of managing their development environment.

Machine learning is still filled with many problems when it comes to configuration. There isn’t a single cohesive system that works by itself out-of-the-box. That means every project needs to be configured to whatever the data scientist likes. If you are working in a team, this configuration problem becomes a major issue. CI/CD has helped somewhat, but they are still not enough. Containers allow scientists to create a single development environment that they can share relatively easily. The role container technology plays is only going to grow.

Improve Collaboration

Containers make it possible for data scientists to collaborate closely when developing machine learning models. Before containers, a huge problem was the inability to work together because of differences in configuration. You all worked in different environments, making it difficult to fix bugs. Was the bug caused by the environment? This question created a lot of problems for people developing and deploying models.

Model validation became a major issue because you didn’t know whether your environment played a role in the results you got. Containers make it really easy for you to give everyone the same platform to get started. You can then figure out whether problems are caused by the platform or your code.

Make Platform-Agnostic Deployment a Reality

When it comes to model validation and being able to deploy models, a big piece of the puzzle is being able to deploy that model to almost any platform. Container technology makes it really easy to do this. You can package your finished model and associated code into an easy to deploy package. Your container can also be made to be compatible with multiple cloud providers. This gives you the flexibility to deploy almost anywhere. It also makes it possible to easily switch out your model when upgrades arrive. Finally, this technology makes it easy for you to move from one platform to the next if you aren’t happy.

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