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Scale ML workflows with Amazon SageMaker Studio and Amazon SageMaker HyperPod

AWS Machine Learning Blog

Scaling machine learning (ML) workflows from initial prototypes to large-scale production deployment can be daunting task, but the integration of Amazon SageMaker Studio and Amazon SageMaker HyperPod offers a streamlined solution to this challenge. ML SA), Monidipa Chakraborty (Sr. Delete the IAM role you created.

ML 101
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Apple's AI-Powered Siri Is Such a Disaster That Employees Have Given the Team Developing It a Rude Nickname

Flipboard

Known as AI/ML for short, its woes only deepened after Apple announced that it had to delay its much-hyped next iteration of AI enhancements for Siri until 2026. The moniker is also a jab at AI/ML's ousted leaders. Federighi has led Apple's engineering team since 2012, earning a reputation for efficiency and execution.

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Feature Platforms?—?A New Paradigm in Machine Learning Operations (MLOps)

IBM Data Science in Practice

The growth of the AI and Machine Learning (ML) industry has continued to grow at a rapid rate over recent years. Hidden Technical Debt in Machine Learning Systems More money, more problems — Rise of too many ML tools 2012 vs 2023 — Source: Matt Turck People often believe that money is the solution to a problem.

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Create a data labeling project with Amazon SageMaker Ground Truth Plus

AWS Machine Learning Blog

In addition to traditional custom-tailored deep learning models, SageMaker Ground Truth also supports generative AI use cases, enabling the generation of high-quality training data for artificial intelligence and machine learning (AI/ML) models. The following diagram illustrates the solution architecture.

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Govern generative AI in the enterprise with Amazon SageMaker Canvas

AWS Machine Learning Blog

Launched in 2021, Amazon SageMaker Canvas is a visual point-and-click service that allows business analysts and citizen data scientists to use ready-to-use machine learning (ML) models and build custom ML models to generate accurate predictions without writing any code. This way, users can only invoke the allowed models.

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Publish predictive dashboards in Amazon QuickSight using ML predictions from Amazon SageMaker Canvas

AWS Machine Learning Blog

Quick iteration and faster time-to-value can be achieved by providing these analysts with a visual business intelligence (BI) tool for simple analysis, supported by technologies like machine learning (ML). Through this capability, ML becomes more accessible to business teams so they can accelerate data-driven decision-making.

ML 101
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Build high-performance ML models using PyTorch 2.0 on AWS – Part 1

AWS Machine Learning Blog

PyTorch is a machine learning (ML) framework that is widely used by AWS customers for a variety of applications, such as computer vision, natural language processing, content creation, and more. With the recent PyTorch 2.0 release, AWS customers can now do same things as they could with PyTorch 1.x Refer to PyTorch 2.0: on AWS PyTorch2.0

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