The AWS Machine Learning Operations (MLOps) framework is an iterative and repetitive process for evolving AI models over time. Like DevOps, practitioners gain efficiencies promoting their artifacts through various environments (such as quality assurance, integration, and production) for quality control. In parallel, customers rapidly adopt multi-account strategies through AWS Organizations and AWS Control Tower to create secure, isolated environments. This combination can introduce challenges for implementing MLOps with AWS pre-trained AI Services, such as Amazon Rekognition Custom Labels. This post discusses design patterns for reducing that complexity while still maintaining security best practices.
Customers across every industry vertical recognize the value of operationalizing machine learning (ML) efficiently and reducing the time to deliver business value. Most AWS pre-trained AI Services address this situation through out-of-the-box capabilities for computer vision, translation, and fraud detection, among other common use cases. Many use cases require domain-specific predictions that go beyond generic answers.

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