AWS Machine Learning Blog

JULY 24, 2023

This allows us to handle more inference requests with fewer accelerators. The AWS Neuron SDK allows us to utilize each of the NeuronCores in parallel, which gives us more control in loading and inferring four or more models in parallel without sacrificing throughput.

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Heartbeat

SEPTEMBER 7, 2023

Consider healthcare, where AI models are being used for disease diagnosis. Understanding how a model arrived at a diagnosis isn’t just a matter of trust; it’s about the patient’s well-being. Think of it as a meticulously organized lab notebook, but one that can handle the complexity of modern ML workflows.

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AWS Machine Learning Blog

MARCH 1, 2023

This post is co-written with Suhyoung Kim, General Manager at KakaoGames Data Analytics Lab. In this post, we share how Kakao Games and the Amazon Machine Learning Solutions Lab teamed up to build a scalable and reliable LTV prediction solution by using AWS data and ML services such as AWS Glue and Amazon SageMaker.

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Dataconomy

JUNE 8, 2023

Moreover, AI technology empowers doctors to delve deep into patients’ lab reports, scans, and medical records, enabling the development of highly customized treatment plans at a micro level. This early detection not only leads to faster interventions but also holds the promise of improved outcomes and prognoses.

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Snorkel AI

OCTOBER 12, 2023

We hope that you will enjoy watching the videos and learning more about the impact of LLMs on the world. In this presentation, center NLP Engineer James Dunham shares takeaways from the half-dozen project teams who used Snorkel in the past year.

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Snorkel AI

OCTOBER 12, 2023

We hope that you will enjoy watching the videos and learning more about the impact of LLMs on the world. In this presentation, center NLP Engineer James Dunham shares takeaways from the half-dozen project teams who used Snorkel in the past year.

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O'Reilly Media

OCTOBER 11, 2022

As a trend, we’re openly skeptical about Hyperautomation. So we’re skeptical about the term Hyperautomation, but we’re not skeptical about the desire to automate. Our current set of AI algorithms are good enough, as is our hardware; the hard problems are all about data.

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