How to Deploy ML Models in Production (Flawlessly)

Author(s): Richard Warepam

Originally published on Towards AI.

4 Things to Keep in Mind Before Deploying Your ML Models

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As a Cloud Engineer, I’ve recently collaborated with a number of project teams, and my primary contribution to these teams has been to do the DevOps duties required on the GCP Cloud.

To learn more about me, read the following:

Am I Going in the Right Direction?

medium.com

Regardless of the project, it might be software development or ML Model building. My main goal as a DevOps Cloud Engineer is to achieve four objectives. What are they?

ReliabilityScalabilitySecurity andMaintainability

In this article, I’ll highlight four things you should bear in mind while deploying your ML models in production because the framework I’m providing will help you achieve all four of the goals I described before.

First and foremost, how can we ensure the reliability of machine learning models? I’ll say, employ version control systems.

But how does it do? To better understand this, let’s define version control systems. Version control is used to keep track of different versions of your software or models.

So, if we can track and control these versions when a version fails after production, we can still utilize the most stable… Read the full blog for free on Medium.

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Published via Towards AI