APIs Examples Groups Scripts 
AWS Machine Learning
MAY 16, 2024
The main AWS services used are SageMaker, Amazon EMR , AWS CodeBuild , Amazon Simple Storage Service (Amazon S3), Amazon EventBridge , AWS Lambda , and Amazon API Gateway. Real-time recommendation inference The inference phase consists of the following steps: The client application makes an inference request to the API gateway.
AWS Machine Learning
DECEMBER 21, 2022
Depending on the design of your feature groups and their scale, you can experience training query performance improvements of 10x to 100x by using this new capability. SageMaker Feature Store automatically builds an AWS Glue Data Catalog during feature group creation. Creating feature groups using Iceberg table format.
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AWS Machine Learning
AUGUST 4, 2023
Although we use a specific algorithm to train the model in our example, you can use any algorithm that you find appropriate for your use case. When a version of the model in the Amazon SageMaker Model Registry is approved, the endpoint is exposed as an API with Amazon API Gateway using a custom Salesforce JSON Web Token (JWT) authorizer.
AWS Machine Learning
MAY 31, 2023
In such cases, data scientists have to provide these parameters to their ML model training and deployment code manually, by noting down subnets, security groups, and KMS keys. Provide a name for the stack (for example, networking-stack ), and complete the remaining steps to create the stack.
AWS Machine Learning
FEBRUARY 13, 2024
For example, in an application that recommends a music playlist, features could include song ratings, listening duration, and listener demographics. SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. Features are inputs to ML models used during training and inference.
AWS Machine Learning
OCTOBER 25, 2022
Model weights are available via scripts in the GitHub repository , and the MSAs are hosted by the Registry of Open Data on AWS (RODA). We use aws-do-eks , an open-source project that provides a large collection of easy-to-use and configurable scripts and tools to enable you to provision EKS clusters and run your inference.
AWS Machine Learning
MARCH 23, 2023
To demonstrate this, we show an example of customizing an Amazon SageMaker Scikit-learn, open sourced, deep learning container to enable a deployed endpoint to accept client-side encrypted inference requests. Although this example shows how to perform this for inference operations, you can extend the solution to training and other ML steps.
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