Remove 12 how-to-optimize-the-performance-of-aws-s3
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Generative AI and multi-modal agents in AWS: The key to unlocking new value in financial markets

AWS Machine Learning Blog

Financial organizations generate, collect, and use this data to gain insights into financial operations, make better decisions, and improve performance. Multi-modal data is a valuable component of the financial industry, encompassing market, economic, customer, news and social media, and risk data.

AWS 101
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Getir end-to-end workforce management: Amazon Forecast and AWS Step Functions

AWS Machine Learning Blog

In this post, we describe the end-to-end workforce management system that begins with location-specific demand forecast, followed by courier workforce planning and shift assignment using Amazon Forecast and AWS Step Functions. These predictions, produced with Amazon Forecast, help determine when and how many couriers each warehouse needs.

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Efficiently fine-tune the ESM-2 protein language model with Amazon SageMaker

AWS Machine Learning Blog

In this post, we demonstrate how to efficiently fine-tune a state-of-the-art protein language model (pLM) to predict protein subcellular localization using Amazon SageMaker. Name Manufacturer 2022 Global Sales ($ billions USD) Indications Comirnaty Pfizer/BioNTech $40.8 Top companies and drugs by sales in 2022.

AWS 93
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How Light & Wonder built a predictive maintenance solution for gaming machines on AWS

AWS Machine Learning Blog

Working with AWS, Light & Wonder recently developed an industry-first secure solution, Light & Wonder Connect (LnW Connect), to stream telemetry and machine health data from roughly half a million electronic gaming machines distributed across its casino customer base globally when LnW Connect reaches its full potential.

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How Getir reduced model training durations by 90% with Amazon SageMaker and AWS Batch

AWS Machine Learning Blog

In this post, we explain how we built an end-to-end product category prediction pipeline to help commercial teams by using Amazon SageMaker and AWS Batch , reducing model training duration by 90%. Established in 2015, Getir has positioned itself as the trailblazer in the sphere of ultrafast grocery delivery.

AWS 94
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Demand forecasting at Getir built with Amazon Forecast

AWS Machine Learning Blog

In this post, we describe how we used Forecast to achieve these benefits. We outline how we built an automated demand forecasting pipeline using Forecast and orchestrated by AWS Step Functions to predict daily demand for SKUs. Getir is the pioneer of ultrafast grocery delivery.

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Unlock ML insights using the Amazon SageMaker Feature Store Feature Processor

AWS Machine Learning Blog

For many ML use cases, raw data like log files, sensor readings, or transaction records need to be transformed into meaningful features that are optimized for model training. For this use case, we see how SageMaker Feature Store helps convert the raw car sales data into structured features. Remote runs at scale using Spark.

ML 93