How to Create Amazon Bedrock Custom Models Using Terraform and Infrastructure as Code (IaC)
In the world of artificial intelligence (AI), customization is key for creating unique user experiences that reflect a company’s identity and services. Amazon Bedrock offers a solution for customizing foundation models with your own data, allowing you to build applications specific to your domain, organization, and use case. To streamline the process of data retrieval, formatting, and model customization, infrastructure as code (IaC) can be used.
In a recent blog post published by AWS, the process of creating an Amazon Bedrock custom model using HashiCorp Terraform was detailed. This approach allows for automation of the process, ensuring repeatability and versioning as needed. The post outlined the steps to follow, from creating and initializing a Terraform project, to preparing datasets, uploading data to Amazon S3, and customizing models using fine-tuning and continued pre-training.
Terraform, as an IaC tool, provides the benefits of automation, versioning, and repeatability. By defining the process in Terraform code, data science teams can conduct A/B testing and repeatable experiments with ease. The use of Python scripts in conjunction with Terraform allows for data manipulation and preparation, ensuring that the datasets are in the correct format for model customization.
Once the custom model is created, users can configure Provisioned Throughput for the models to test and deploy them for wider usage. By following the steps outlined in the blog post, users can create custom models that cater to their specific needs and requirements. The post also emphasized best practices and considerations when using this solution, such as data privacy, network security, billing, and availability of customization options.
In conclusion, the blog post demonstrated a practical approach to creating Amazon Bedrock custom models using Terraform, showcasing how infrastructure as code can streamline the process of model customization. By leveraging these tools and methodologies, data science teams can efficiently build domain-specific models and conduct experiments to enhance their AI applications. Whether for fine-tuning existing models or continued pre-training, the solution provided a comprehensive guide for users looking to customize their AI models securely and efficiently.