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Deploy Amazon SageMaker pipelines using AWS Controllers for Kubernetes

AWS Machine Learning Blog

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.

DevOps 96
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How Veritone uses Amazon Bedrock, Amazon Rekognition, Amazon Transcribe, and information retrieval to update their video search pipeline

AWS Machine Learning Blog

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.

Metadata 122
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Create Multi-Lingual Subtitles with AssemblyAI and DeepL

AssemblyAI

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.

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How Forethought saves over 66% in costs for generative AI models using Amazon SageMaker

AWS Machine Learning Blog

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.

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Empowering Model Sharing, Enhanced Annotation, and Azure Blob Backups in NLP Lab

John Snow Labs

In this release, we’ve focused on simplifying model sharing, making advanced features more accessible with FREE access to Zero-shot NER prompting, streamlining the annotation process with completions and predictions merging, and introducing Azure Blob backup integration. Click “Submit” to finalize.

NLP 52
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Get started quickly with AWS Trainium and AWS Inferentia using AWS Neuron DLAMI and AWS Neuron DLC

AWS Machine Learning Blog

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

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Time series forecasting with Amazon SageMaker AutoML

AWS Machine Learning Blog

In the training phase, CSV data is uploaded to Amazon S3, followed by the creation of an AutoML job, model creation, and checking for job completion. All other columns in the dataset are optional and can be used to include additional time-series related information or metadata about each item.