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DevOps engineers often use Kubernetes to manage and scale ML applications, but before an ML model is available, it must be trained and evaluated and, if the quality of the obtained model is satisfactory, uploaded to a model registry. SageMaker simplifies the process of managing dependencies, container images, auto scaling, and monitoring.
Machine Learning Operations (MLOps): Overview, Definition, and Architecture” By Dominik Kreuzberger, Niklas Kühl, Sebastian Hirschl Great stuff. If you haven’t read it yet, definitely do so. Lived through the DevOps revolution. If you’d like a TLDR, here it is: MLOps is an extension of DevOps. Some are my 3–4 year bets.
Amazon ECS configuration For Amazon ECS, create a task definition that references your custom Docker image. dkr.ecr.amazonaws.com/ : ", "essential": true, "name": "training-container", } ] } This definition sets up a task with the necessary configuration to run your containerized application in Amazon ECS. neuronx-py310-sdk2.18.2-ubuntu20.04
Problem definition Traditionally, the recommendation service was mainly provided by identifying the relationship between products and providing products that were highly relevant to the product selected by the customer. When training is complete (through the Lambda step), the deployed model is updated to the SageMaker endpoint.
Create a KMS key in the dev account and give access to the prod account Complete the following steps to create a KMS key in the dev account: On the AWS KMS console, choose Customer managed keys in the navigation pane. Under Advanced Project Options , for Definition , select Pipeline script from SCM. Choose Create key. Choose Save.
Amazon Q Business is a generative AI-powered assistant that can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in your enterprise systems. Prerequisites Complete the following prerequisites: Have a valid AWS account. Upload the sample articles file to the S3 bucket.
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