AWS Machine Learning Blog

AUGUST 20, 2024

You can call the SageMaker ListWorkteams or DescribeWorkteam APIs to view workteams’ metadata, including the WorkerAccessConfiguration. He works to help AWS customers leverage machine learning applications, including prior work on ML-based fraud detection with Amazon Fraud Detector.

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Software Engineer ML Machine Learning Metadata 110

AWS Machine Learning Blog

DECEMBER 13, 2023

ML operations, known as MLOps, focus on streamlining, automating, and monitoring ML models throughout their lifecycle. Data scientists, ML engineers, IT staff, and DevOps teams must work together to operationalize models from research to deployment and maintenance.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

In AWS, these model lifecycle activities can be performed over multiple AWS accounts (for example, development, test, and production accounts) at the use case or business unit level. It also helps achieve data, project, and team isolation while supporting software development lifecycle best practices. Madhubalasri B.

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AWS Machine Learning Blog

MAY 5, 2023

Solution overview Ground Truth is a fully self-served and managed data labeling service that empowers data scientists, machine learning (ML) engineers, and researchers to build high-quality datasets. For our example use case, we work with the Fashion200K dataset , released at ICCV 2017.

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Metadata Computer Vision Machine Learning Data Scientist 90

AWS Machine Learning Blog

SEPTEMBER 29, 2023

Planet and AWS’s partnership on geospatial ML SageMaker geospatial capabilities empower data scientists and ML engineers to build, train, and deploy models using geospatial data. This is a powerful tool for data scientists and developers who want to work with satellite imagery and other geospatial data.

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AWS Machine Learning Blog

NOVEMBER 16, 2023

Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. In this post, we describe how Philips partnered with AWS to develop AI ToolSuite—a scalable, secure, and compliant ML platform on SageMaker.

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AWS Machine Learning Blog

JUNE 8, 2023

A session stores metadata and application-specific data known as session attributes. Victor Rojo is a highly experienced technologist who is passionate about the latest in AI, ML, and software development. Ryan Gomes is a Data & ML Engineer with the AWS Professional Services Intelligence Practice.

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The MLOps Blog

MARCH 15, 2023

Cost and resource requirements There are several cost-related constraints we had to consider when we ventured into the ML model deployment journey Data storage costs: Storing the data used to train and test the model, as well as any new data used for prediction, can add to the cost of deployment. S3 buckets. Redshift, S3, and so on.

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The MLOps Blog

MARCH 21, 2023

As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale. In this comprehensive guide, we’ll explore everything you need to know about machine learning platforms, including: Components that make up an ML platform.

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AWS Machine Learning Blog

APRIL 19, 2024

of the SageMaker ACK Operators adds support for inference components , which until now were only available through the SageMaker API and the AWS Software Development Kits (SDKs). These controllers allow Kubernetes users to provision AWS resources like buckets, databases, or message queues simply by using the Kubernetes API.

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Metadata LLM Software Development Machine Learning 121