7 Steps of Migrating Model in Cloud

Last Updated : 16 May, 2023

Migrating a model to a cloud can help in several ways, such as improving scalability, flexibility, and accessibility. There are seven steps to follow when migrating a model to the cloud:

Step 1: Choose the right cloud provider ( Assessment step)

The first step in migrating your model to the cloud is to choose a cloud provider that aligns with your needs, budget, and model requirement. consider the factors such as compliance, privacy, and security.

Step 2: Prepare your data ( Isolation step)

Before migrating to your cloud, you need to prepare your data. for that ensure your data is clean and well organized, and in a format that is compatible with your chosen cloud provider.

Step 3: Choose your cloud storage ( Mapping step)
Once your data is prepared, you need to choose your cloud storage. This is where your data is stored in the cloud. there are many cloud storage services such as GCP Cloud Storage, AWS S3, or Azure Blob Storage.

Step 4: Set up your cloud computing resources and deploy your model ( Re- architect step)
If you want to run a model in the cloud, you will need to set up your cloud computing resources. This includes selecting the appropriate instance type and setting up a virtual machine(VM) or container for your model. After setting up your computing resource, it is time to deploy your model to the cloud. This includes packaging your model into a container or virtual machine image and deploying it to your cloud computing resource. and while deploying it may be possible that some functionality gets lost so due to this some parts of the application need to be re-architect.

Step-5: Augmentation step

It is the most important step for our business for which we migrate to the cloud in this step by taking leverage of the internal features of cloud computing service we augment our enterprise.

Step 6: Test your Model

Once your model is deployed, we need to test it to ensure that it is working or not. That involves running test data through your model and comparing the results with your expected output.

Step 7: Monitor and maintain your Model

After the model is deployed and tested, it is important to monitor and maintain it. That includes monitoring the performance, updating the model as needed, and need to ensure your data stays up-to-date. Migrating your machine learning model to the cloud can be a complex process, but above 7 steps, you can help ensure a smooth and successful migration, ensuring that your model is scalable and accessible.