Deployment and Orchestration of ML Workflows — AWS Certified Machine Learning Engineer - Associate

Official references for this objective

• Amazon Web Services — Best practices and design patterns for building machine learning workflows with Amazon SageMaker Pipelines | Artificial Intelligence

  You can run a pipeline in local mode using the LocalPipelineSession context. In this mode, the pipeline and jobs are run locally using resources on

• Amazon Web Services — Adapt your own inference container for Amazon SageMaker AI - Amazon SageMaker AI

  If you want to use SageMaker AI hosting services for inference, you must create a model , create an endpoint config and create an endpoint

• Amazon Web Services — Enhance your machine learning development by using a modular architecture with Amazon SageMaker projects | Artificial Intelligence

  The EventBridge rule pattern monitors the events PutObject and CompleteMultipartUpload

• Amazon Web Services — Automation pipelines - Build a Secure Enterprise Machine Learning Platform on AWS

  The CloudFormation script first creates an AWS Step Functions state machine workflow consisting of a SageMaker AI processing step, a SageMaker AI model training step,

• Amazon Web Services — Building machine learning workflows with Amazon SageMaker Processing jobs and AWS Step Functions | Artificial Intelligence

  This polling mechanism also incurs an additional cost because of the state transitions for checking the status.

• Amazon Web Services — Model Hosting FAQs - Amazon SageMaker AI

  Real-Time Inference is suitable for workloads with millisecond latency requirements, payload sizes up to 25 MB

• Amazon Web Services — SageMaker AI Workflows - Amazon SageMaker AI

  With SageMaker AI components for Kubeflow pipelines, you can create and monitor native SageMaker AI jobs from your Kubeflow Pipelines.

• Amazon Web Services — Use SageMaker AI-Provided Project Templates - Amazon SageMaker AI

  There is a manual approval step between the staging and production build steps, so that a MLOps engineer must approve the model before it is