Favorite You can deploy machine learning (ML) models for real-time inference with large libraries or pre-trained models. Common use cases include sentiment analysis, image classification, and search applications. These ML jobs typically vary in duration and require instant scaling to meet peak demand. You want to process latency-sensitive inference requests

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Shared by AWS Machine Learning September 29, 2021

Favorite This is a guest post by Erin Stanton, who currently runs the Global Client Support organization for Virtu Analytics.  Virtu Financial is a leading provider of financial services and products that uses cutting-edge technology to deliver liquidity to the global markets and innovative, transparent trading solutions to its clients.

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Shared by AWS Machine Learning September 29, 2021

Favorite Based in Dubai, Ounass is the Middle East’s leading ecommerce platform for luxury goods. Scouring the globe for leading trends, Ounass’s expert team reports on the latest fashion updates, coveted insider information, and exclusive interviews for customers to read and shop. With more than 230,000 unique catalog items spanning

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Shared by AWS Machine Learning September 27, 2021

Favorite Proactively detecting unusual or unexpected variances in your business metrics and reducing false alarms can help you stay on top of sudden changes and improve your business performance. Accurately identifying the root cause of deviation from normal business metrics and taking immediate steps to remediate an anomaly can not

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Shared by AWS Machine Learning September 27, 2021

Favorite Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. It provides all the tools you need to take your models from experimentation to production while boosting your productivity. You can write code,

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Shared by AWS Machine Learning September 24, 2021

Favorite Conversational interfaces have the potential to allow customers to interact more naturally with automated systems. From virtual assistants to concierge chatbots, conversational interfaces can bring convenience and personalization to customers at scale. However, these benefits depend not only on the technology that the Amazon Lex platform and other AWS

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Shared by AWS Machine Learning September 24, 2021

Favorite When we analyze images, we may want to incorporate other metadata related to the image. Examples include when and where the image was taken, who took the image, as well as what is featured in the image. One way to represent this metadata is to use a JSON format,

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Shared by AWS Machine Learning September 23, 2021

Favorite This is a guest post by Shae Selix (Staff Data Scientist at Calm) and Luis Lopez Soria (Sr. AI/ML Specialist SA at AWS). Today, content is proliferating. It’s being produced in many different forms by a host of content providers, both large and small. Whether it’s on-demand video, music,

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Shared by AWS Machine Learning September 23, 2021

Favorite In the era of Big Data, businesses are faced with a deluge of time series data. This data is not just available in high volumes, but is also highly nuanced. Amazon Forecast Deep Learning algorithms such as DeepAR+ and CNN-QR build representations that effectively capture common trends and patterns

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Shared by AWS Machine Learning September 23, 2021

Favorite Modern quantitative finance is based around the approach of pattern recognition in historical data. This approach requires teams of scientists to work in a collaborative and regulated setting in order to develop models that can be used to make trading predictions. With the growing influence of this field, both

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Shared by AWS Machine Learning September 22, 2021