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AWS Machine Learning Blog
SEPTEMBER 4, 2024
SageMaker simplifies the process of managing dependencies, container images, auto scaling, and monitoring. This configuration takes the form of a Directed Acyclic Graph (DAG) represented as a JSON pipeline definition. In SageMaker, ML engineers can use the SageMaker Python SDK to generate a pipeline definition in JSON format.
AWS Machine Learning Blog
MAY 7, 2024
Veritone’s current media search and retrieval system relies on keyword matching of metadata generated from ML services, including information related to faces, sentiment, and objects. We use the Amazon Titan Text and Multimodal Embeddings models to embed the metadata and the video frames and index them in OpenSearch Service.
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AssemblyAI
JULY 8, 2024
Before you start To complete this tutorial, you'll need: An upgraded AssemblyAI account A DeepL API account. It returns metadata about the submitted transcription, from which the ID is used to set the ID of the Job. You'll then use DeepL to translate the subtitles into different languages.
AWS Machine Learning Blog
JUNE 11, 2024
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
AWS Machine Learning Blog
JUNE 13, 2023
In addition, all SageMaker real-time endpoints benefit from built-in capabilities to manage and monitor models, such as including shadow variants , auto scaling , and native integration with Amazon CloudWatch (for more information, refer to CloudWatch Metrics for Multi-Model Endpoint Deployments ). 2xlarge instances.
AWS Machine Learning Blog
APRIL 16, 2024
Connection definition JSON file When connecting to different data sources in AWS Glue, you must first create a JSON file that defines the connection properties—referred to as the connection definition file. As of this writing, the only supported mechanism of creating these connections is using the AWS Command Line Interface (AWS CLI).
AWS Machine Learning Blog
MAY 31, 2023
Each model deployed with Triton requires a configuration file ( config.pbtxt ) that specifies model metadata, such as input and output tensors, model name, and platform. Set up your environment To set up your environment, complete the following steps: Launch a SageMaker notebook instance with a g5.xlarge xlarge instance.
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