{"id":1539,"date":"2022-02-08T18:03:49","date_gmt":"2022-02-08T18:03:49","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2022\/02\/08\/implement-mlops-using-aws-pre-trained-ai-services-with-aws-organizations\/"},"modified":"2022-02-08T18:03:49","modified_gmt":"2022-02-08T18:03:49","slug":"implement-mlops-using-aws-pre-trained-ai-services-with-aws-organizations","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2022\/02\/08\/implement-mlops-using-aws-pre-trained-ai-services-with-aws-organizations\/","title":{"rendered":"Implement MLOps using AWS pre-trained AI Services with AWS Organizations"},"content":{"rendered":"
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The AWS Machine Learning Operations (MLOps) framework<\/a> 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<\/a> and AWS Control Tower<\/a> 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<\/a>. This post discusses design patterns for reducing that complexity while still maintaining security best practices.<\/p>\n

Overview<\/h2>\n

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. The AI Services can fine-tune the predictive model results using customer-labeled data for those scenarios.<\/p>\n

Over time, the domain-specific vocabulary changes and evolves. For example, suppose a tool manufacturer creates a computer vision model to detect its products in images (such as hammers and screwdrivers). In a future release, the business adds support for wrenches and saws. These new labels necessitate code changes on the manufacturer\u2019s websites and custom applications. Now, there are dependencies that both artifacts must release simultaneously.<\/p>\n

The AWS MLOps framework addresses these release challenges through iterative and repetitive processes. Before reaching production end-users, the model artifacts must traverse various quality gates like application code. You typically implement those quality gates using multiple AWS accounts<\/a> within an AWS organization. This approach gives the flexibility to centrally manage these application domains and enforce guardrails and business requirements. It\u2019s becoming increasingly common to have tens or even hundreds of accounts within your organization. However, you must balance your workload isolation needs against the team size and complexity.<\/p>\n

MLOps practitioners have standard procedures for promoting artifacts between accounts (such as QA to production). These patterns are straightforward to implement, relying on copying code and binary resources between Amazon Simple Storage Service<\/a> (Amazon S3) buckets. However, AWS pre-trained AI Services don\u2019t currently support copying the trained custom model across AWS accounts. Until such a mechanism exists, you need to retrain the models in each AWS account using the same dataset. This approach involves time and cost for retraining the model in a new account. This mechanism can be a viable option for some customers. However in this post, we demonstrate the means to define and evolve these custom models centrally while securely sharing them across an AWS organization\u2019s accounts.<\/p>\n

Solution overview<\/h2>\n

This post discusses design patterns for securely sharing AWS pre-trained AI Service domain-specific models. These services include Amazon Fraud Detector<\/a>, Amazon Transcribe<\/a>, and Amazon Rekognition<\/a>, to name a few. Although these strategies are broadly applicable, we focus on Rekognition Custom Labels as a concrete example. We intentionally avoid diving too deep into Rekognition Custom Labels-specific nuances.<\/p>\n

The architecture begins with a configured AWS Control Tower in the management account. AWS Control Tower provides the easiest way to set up and govern a secure, multi-account AWS environment. As shown in the following diagram, we use Account Factory<\/a> in AWS Control Tower to create five AWS accounts:<\/p>\n