Model deployment is the final step in the process where you put everything you have done to a real test. To deploy ML models requires a deep understanding of the production environment. That is why machine learning deployment is one of the major stumbling blocks when building machine learning models.
Machine learning model deployment might sound easy, but it is usually a major stumbling block that causes many companies to abandon their ML initiatives. These companies never seem to grasp how difficult it is to deploy data science models to production. Many companies don’t even know what does it mean to deploy a machine learning model. However, they quickly learn when they are faced with the prospect of wasting thousands of dollars and many hours on something that failed spectacularly.
The Meaning of Model Deployment
The thing about deploying machine learning models is that many people don’t even know what that means. To deploy a machine learning model means integrating that model into your existing application. You will then feed it inputs, and you will be provided outputs as defined by your use case. For example, you can deploy ML models built for predicting whether a picture features a dog or cat. You would then feed it pictures of dogs or cats, and it will tell you which one it is. You can also feed other pictures, and if it has been well-trained, it will tell you that he has found nothing there.
Deploying ML models is more than this, as you need to worry about other production issues that come into play. Machine learning model deployment also involves thinking about how scalable and portable your application will be. How well can you move it to other systems? These things need to be taken into consideration. You also need to look at the architecture of the system as well.
What an ML System Looks Like
When you deploy model to production, you also have to think about how the architecture fits into what you are trying to achieve. For example, there are various layers that you need to look at when deploying data science models. For example, there is the data layer that is needed to ensure your model works correctly. You also need a layer focusing on your features.
There are also the scoring and evaluation layers, and they help turn predictions and features into answers that will make sense for your use case. Understanding how the system creates a cohesive whole is one of the hallmarks of deploying machine learning models.
Considerations for Model Deployment
The final step is to figure out how to actually deploy your machine learning models. Deploying machine learning models isn’t as easy as you think, as you have to figure out how you will improve and have your machine learning model work in production. For example, do you train your model once? Do you constantly update it periodically? Or maybe you are training and improving your machine learning model in real-time. This is a major thing to think about, as it affects how well you will be able to maintain things. It also affects the performance of your application when you go to deploy ML models in the future.