The Complications with Deploying an AI Model


The Complications with Deploying an AI Model Team
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As you know, the significant majority of machine learning models never make it into production. Model deployment is inherently difficult, and it gets even harder when it comes to more complicated models. The main thing you need to know about an AI ML model is how difficult it is for professionals to get things to work correctly. In fact, up to 90% of models never make it to that stage. They call this the valley of death.

The major problem in ML systems is that the majority of the code is actually ML code. You end up building an entire system with many moving parts, and the model is only a fraction of those moving parts. Because of this, many people don’t actually do AI model deployment the right way.

Model Creation

The big thing about developing a model and bringing it to production is having a good understanding of the entire process. Model deployment isn’t an event, as there is a lot that goes into doing it. It is also continuous, as ML systems are not perfect the first time they are created.

The main thing you need to do here is to do all the prerequisite tasks that data scientists need. However, you will need to have a system that is tailored to give you the best possible solution. The main problem with current solutions is that they don’t offer the ease of use that people are used to in many other fields. Data science is difficult, and it is even harder when you have to start from scratch without knowing how these complicated tools work.

Model Deployment

The significant other problem is deploying your model. This is typically the domain of a machine learning engineer, and many infrastructure considerations need to occur. However, many ML systems are now builtin the cloud, meaning that you don’t have to worry about the hardware side of it all. However, a major problem here is that you might not want to depend on the systems that other people have set up. It is also difficult setting up the software environment for your production application. This is typically where people struggle during machine learning model deployment. Model deployment is difficult, but there are a few ways to make it easy

Model Scaling

Eventually, you get to a level where your model has been deployed successfully. However, you still have the challenge of scaling your production application to meet challenges in the future. The best possible outcome is to have a solution that does all the things you need. For example, infrastructure management, also scaling, monitoring, drift detection, and model health checks. A good solution like is key because it allows you to focus on building the best model possible. It means your small team can still be effective at making a model get to production successfully.

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

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