When to use MLOps for IoT Edge

This article discusses how to decide if MLOps for IoT Edge is the right choice for your machine learning applications. We'll analyze the suitability based on the following criteria:

Collaborative considerations

MLOps enables data scientists and developers to collaborate using the same DevOps processes. Most teams need MLOps to collaborate and speed up model development and deployment. Even small teams, such as those with five developers, can use MLOps to foster good engineering practices and future scalability.

Frequency of model refresh

How frequently do you need to update models in production? If your data changes rapidly and your model needs frequent updates, consider MLOps because it automates the model retraining process.

IoT considerations

Do you use IoT Edge on multiple devices where you need to deploy and refresh machine learning models? MLOps for IoT Edge is suited for this solution.

Scalability considerations

A build pipeline on Azure DevOps can be scaled for applications of any size. Hence, MLOps is suited for solutions that need to be scaled in the future.

Cost considerations

Azure DevOps is free for open-source projects and small projects with up to five users, but larger teams need a purchase plan based on the number of users. Depending on the use case, compute is the most significant cost driver in this architecture. You should explore the cost considerations depending on the use case.

Governance data requirements

MLOps captures governance data for an end-to-end model build and deployment. This data can help for interpretability, regulatory compliance, and audits.

Degree of automation needed for your ML lifecycle

Different components of MLOps enable automation, such as CI/CD. Collectively, MLOps can automate the end-to-end processes.