How do you deploy your ML model ?

Introduction

In 2023, the global Machine Learning market was worth $33.9 billion and is expected to reach $126.7 billion by 2028.

Deploying a Machine Learning (ML) model is the final phase of the model development process. It involves making the model available for use in production. Deploying an ML model has a number of advantages, in particular :

The model can be used to make decisions in the real world.

  • It enables repetitive tasks to be automated.
  • It saves time and money.
  • The deployment of an ML model can be

1) Preparing the model

Before you can deploy your model, you need to prepare it. This means ensuring that the model is ready to be used in production. This includes :

  • Ensure that the model is well trained and has acceptable performance.
  • Ensure that the model is well documented, so that users can understand and use it.
  • Ensure that the model is compatible with the production environment.

2) Choosing a deployment target

The deployment target is where your model will be run in production. There are two main deployment target options :

  • On-premise : the model is deployed on servers or virtual machines located in your data centre.
  • In the cloud : the model is deployed on servers or virtual machines located in the cloud.

The choice of deployment target depends on your specific requirements. If you need total control over the model’s execution environment, you can choose to deploy it on site. If you want a more flexible and scalable solution, you can choose to deploy it in the cloud.

3) Creating a runtime environment

  • The execution environment is the system that will run your model in production. This includes the hardware, software and resources required for the model to function properly.
  • If you are deploying your model on-premise, you will need to configure the runtime environment yourself. If you deploy your model in the cloud, you can use a Machine Learning (ML) platform that provides a pre-configured runtime environment.

4) Deploying the model

Once you have prepared the model, chosen a deployment target and created a runtime environment, you can deploy the model.

The deployment process varies depending on the deployment target and the ML platform you are using. However, the following general steps are generally involved :

  • Register the model : before you can deploy the model, you need to register it in your ML workspace. This allows the ML platform to track the model and make it available for deployment.
  • Prepare the inference script : the inference script is the code that executes the model in production. You need to prepare the inference script according to the deployment target and the ML platform you are using.
  • Define the inference configuration : the inference configuration specifies the parameters of the model execution environment. You must define the inference configuration according to your specific needs.
  • Deploy the model : once you have prepared the inference script and defined the inference configuration, you can deploy the model.

5) Testing the model

Once you have deployed the model, you need to test it to ensure that it works correctly. You can test the model by sending input data and checking the results.

6) Putting the model into production

Once you have tested the model and are satisfied with its performance, you can put it into production. This means making the model available to end users.

To find out more about ALLONIA

ALLONIA : the SaaS artificial intelligence platform that accelerates and secures AI projects for large companies, SMEs, ETIs and public organisations.

ALLONIA is the AI platform that enables companies to develop, deploy and exploit machine learning (ML) models in a secure and collaborative way.

ALLONIA enables you to deploy your AI projects in just a few clicks and makes it easy to share your data and models between your internal teams, as well as with your partners and customers.

Conclusion

Deploying an ML model is an important step in the model development process. By following the steps outlined above, you can successfully deploy your ML model and use it to make decisions in the real world.