Every machine learning practitioner knows that model deployment is the main goal of every project. If you don’t achieve ML model deployment, you are essentially doing research on the company’s dime. Nothing really happens until machine learning deployment is achieved. That is one of many reasons why you need to understand what is required to deploy ML models in the right way. Once you understand how to do it well, you can put yourself in a much more comfortable place.
The sad truth is that about 90% of ML models never make it to production. That means most people are spending time and money to reach no result. ML model deployment is such a valuable part of the process that you need to think about all of it. You have to understand the intricacies needed to deploy ML models in the right way. Deploying models into production is no easy task, and there is a lot of specialized knowledge that should be required to make it happen.
Understanding ML Model Deployment
ML model deployment is all about creating a machine learning model and putting it into live production. That means your model will be responsible for making predictions in a production environment facing the real world. Once you get to this level, many things can go wrong, which is why ML model deployment is such a difficult task. If it was easy, much more than 10% would make it to production successfully.
It requires clever planning and execution in order to make it work well. You also need DevOps processes in place to ensure you have a production workload that will be useful as well. Once you have achieved careful planning in your machine learning project, you are ready to take it to the next stage. There are certain other aspects that need to come together as well when it comes to planning to deploy machine learning model to production.
ML Model Deployment Planning
The main thing to think about is where you will store your data. ML model deployment is difficult, but it is all about successfully organizing data in a way that will enable your model to make predictions. You have to think about the practical ML model deployment tasks required. You need to think about storage, size, and retrieval.
You also have to think about the libraries and software tools you would use when doing machine learning deployment. There are many framework options to make this work, and it is critical to understand how these fit together to form a cohesive whole. Machine learning deployment isn’t the end of the process.
Making ML Model Deployment Efficient
The process of moving your machine learning model to a production environment needs to be as optimized as possible. For that reason, it is critical that you master certain aspects of the process needed to deploy machine learning model. For example, you need steps you follow to ensure it does really well. These steps are:
- Create a training environment during the development
- Optimize while testing your code
- Create a container for deployment
- Create a system to monitor and maintain your model
When it comes to machine learning model deployment, you can either do continuous or batch inference. However, the easiest way to do it correctly is to create a system that makes it easy when deploying using a container. By deploying with a container, the process of moving to a production workload is seamless. While you do all of these things, you can be confident that ML model deployment will be as easy as possible.