Deploying the Application

The deployment phase of the generative AI lifecycle ensures that the trained model is successfully integrated into the target environment for practical use. During this phase, careful consideration is given to factors such as system architecture, scalability, security, and user experience to ensure a seamless and efficient deployment.

Key considerations

There are factors that you should consider when deploying your model on premises or in the cloud. The following list is not exhaustive, but keep these factors in mind as you are deploying your model.

• Cost: Pay for the resources that you use with no minimum fees.
• Regions: Model deployment is limited to certain AWS Regions.
• Quotas: Ensure that you have the adequate service resources for your AWS account.
• Security:
• If your model is deployed in AWS infrastructure, the security responsibility is shared between the company and AWS.If accessing a model outside of AWS, security considerations must be evaluated for data leaving the AWS account.

By carefully navigating the deployment phase, organizations can unlock the full potential of generative AI models. This makes it possible for them to drive innovation, enhance operational efficiency, and deliver exceptional user experiences.

You have now completed the development phase of the generative AI application lifecycle. Continue to the next lesson for the course summary.