AWS Machine Learning Blog

AUGUST 22, 2023

In this post, we discuss how to implement federated learning on Amazon SageMaker to run ML with decentralized training data. In this post, we discuss how to implement federated learning on Amazon SageMaker to run ML with decentralized training data. What is federated learning? Each account or Region has its own training instances.

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The MLOps Blog

OCTOBER 3, 2023

How to transition from data analytics to MLOps engineering Piotr: Miki, you’ve been a data scientist, right? How did you manage to jump from a more analytical, scientific type of role to a more engineering one? How did you manage to jump from a more analytical, scientific type of role to a more engineering one?

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AWS Machine Learning Blog

SEPTEMBER 12, 2023

This is a guest post from Scalable Capital , a leading FinTech in Europe that offers digital wealth management and a brokerage platform with a trading flat rate. Scalable receives hundreds of email inquiries from our clients on a daily basis. Problem statement Scalable Capital is one of the fastest growing FinTechs in Europe.

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AWS Machine Learning Blog

JULY 13, 2023

Recent years have shown amazing growth in deep learning neural networks (DNNs). Amazon SageMaker distributed training jobs enable you with one click (or one API call) to set up a distributed compute cluster, train a model, save the result to Amazon Simple Storage Service (Amazon S3), and shut down the cluster when complete.

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The MLOps Blog

DECEMBER 29, 2022

How would you explain deploying models on GPU in one minute? Knowing how to set up CUDA drivers, there’s a lot of time that you will sink into doing that. Every episode is focused on one specific ML topic, and during this one, we talked to Kyle Morris from Banana about deploying models on GPU. Kyle, to warm you up a little bit.

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AWS Machine Learning Blog

JANUARY 9, 2023

Machine learning (ML) applications are complex to deploy and often require the ability to hyper-scale, and have ultra-low latency requirements and stringent cost budgets. Use cases such as fraud detection, product recommendations, and traffic prediction are examples where milliseconds matter and are critical for business success.

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