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Building an efficient MLOps platform with OSS tools on Amazon ECS with AWS Fargate

AWS Machine Learning

From our experience, artifact server has some limitations, such as limits on artifact size (because of sending it using REST API). Environment variables : Set environment variables, such as model paths, API keys, and other necessary parameters. The main parts we use are tracking the server and model registry.

APIs 120
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What’s All This Fuss About Composability?

ConvergeOne

The underlying technologies of composability include some combination of artificial intelligence (AI), machine learning, automation, container-based architecture, big data, analytics, low-code and no-code development, Agile/DevOps deployment, cloud delivery, and applications with open APIs (microservices).

APIs 79
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Governing the ML lifecycle at scale: Centralized observability with Amazon SageMaker and Amazon CloudWatch

AWS Machine Learning

AWS CloudTrail is also essential for maintaining security and compliance in your AWS environment by providing a comprehensive log of all API calls and actions taken across your AWS account, enabling you to track changes, monitor user activities, and detect suspicious behavior. Enable CloudWatch cross-account observability.

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Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

His expertise spans a broad spectrum, encompassing scalable architectures, distributed computing, big data analytics, micro services and cloud infrastructures for organizations. It also shows how to grant access permissions to existing feature groups at the owner account and share these with another consumer account using AWS RAM.

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Prioritizing employee well-being: An innovative approach with generative AI and Amazon SageMaker Canvas

AWS Machine Learning

You can change the configuration later from the SageMaker Canvas UI or using SageMaker APIs. To explore more about SageMaker Canvas with industry-specific use cases, explore a hands-on workshop. To learn more about SageMaker Data Wrangler in SageMaker Canvas, refer to Prepare Data. Set Instance count to 1. Choose Deploy.

Surveys 121
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A review of purpose-built accelerators for financial services

AWS Machine Learning

In terms of resulting speedups, the approximate order is programming hardware, then programming against PBA APIs, then programming in an unmanaged language such as C++, then a managed language such as Python. SIMD describes computers with multiple processing elements that perform the same operation on multiple data points simultaneously.

Benchmark 106
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What’s All This Fuss About Composability?

ConvergeOne

The underlying technologies of composability include some combination of artificial intelligence (AI), machine learning, automation, container-based architecture, big data, analytics, low-code and no-code development, Agile/DevOps deployment, cloud delivery, and applications with open APIs (microservices).

APIs 40