Deep demand forecasting with Amazon SageMaker

Favorite Every business needs the ability to predict the future accurately in order to make better decisions and give the company a competitive advantage. With historical data, businesses can understand trends, make predictions of what might happen and when, and incorporate that information into their future plans, from product demand

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Shared by AWS Machine Learning June 28, 2022

New built-in Amazon SageMaker algorithms for tabular data modeling: LightGBM, CatBoost, AutoGluon-Tabular, and TabTransformer

Favorite Amazon SageMaker provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning. They can process various

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Shared by AWS Machine Learning June 28, 2022

Inspect your data labels with a visual, no code tool to create high-quality training datasets with Amazon SageMaker Ground Truth Plus

Favorite Launched at AWS re:Invent 2021, Amazon SageMaker Ground Truth Plus helps you create high-quality training datasets by removing the undifferentiated heavy lifting associated with building data labeling applications and managing the labeling workforce. All you do is share data along with labeling requirements, and Ground Truth Plus sets up

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Shared by AWS Machine Learning June 27, 2022

Introducing Amazon CodeWhisperer, the ML-powered coding companion

Favorite We are excited to announce Amazon CodeWhisperer, a machine learning (ML)-powered service that helps improve developer productivity by providing code recommendations based on developers’ natural comments and prior code. With CodeWhisperer, developers can simply write a comment that outlines a specific task in plain English, such as “upload a

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Shared by AWS Machine Learning June 24, 2022

MLOps foundation roadmap for enterprises with Amazon SageMaker

Favorite As enterprise businesses embrace machine learning (ML) across their organizations, manual workflows for building, training, and deploying ML models tend to become bottlenecks to innovation. To overcome this, enterprises needs to shape a clear operating model defining how multiple personas, such as data scientists, data engineers, ML engineers, IT,

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Shared by AWS Machine Learning June 24, 2022