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In a recent publication, the implementation of an artificial intelligence and machine learning environment using Amazon SageMaker Unified Studio was analyzed, following the establishment of the necessary architecture in the first part of the series. This article is aimed at two key profiles in the AI development cycle: administrators, responsible for ensuring governance and infrastructure through automated templates, and data scientists, who use SageMaker to develop models without worrying about managing the underlying infrastructure.
The proposed approach integrates several components into an artificial intelligence operations (AIOps) architecture that facilitates collaboration between administrators and data scientists, allowing for continuous integration and effective delivery of projects. Each stage of the process, from project initialization to production deployment, has been designed to maximize efficiency and traceability.
In the initial phase, the administrator configures the SageMaker Unified Studio environment and implements the necessary infrastructure. Once established, the data scientist can create a new project that automatically triggers the configuration of specific resources via AWS Lambda. This ensures that essential tools and workflows are available from the start of development.
During development, data scientists build, train, and evaluate models using Jupyter notebooks provided by SageMaker. Each pipeline execution is logged to facilitate tracking and experiment management. When a model is approved, an event triggers a Lambda function that coordinates the deployment, ensuring that only models that have passed the relevant validations are promoted to production.
The presented architecture also includes a robust governance and security approach, essential for compliance with regulations. With role-based access controls and workflow automation, the risks of errors and misconfigurations are minimized. Furthermore, mechanisms have been established to audit changes, providing complete traceability throughout the development and deployment process.
On the other hand, SageMaker Catalog plays a key role in managing and accessing data assets, allowing data scientists to subscribe to datasets for use in their machine learning pipelines. This integration, along with a CI/CD approach, enables organizations to operate their AI initiatives more efficiently, reducing the time needed to bring ideas to production while maintaining appropriate security controls.
This practical framework, supported by accessible code in a GitHub repository, presents itself as a valuable tool for organizations seeking to optimize their AI workflows, allowing for a rapid transition from development to deployment while ensuring the necessary governance and compliance.
via: MiMub in Spanish
