Favorite Data fuels machine learning (ML); the quality of data has a direct impact on the quality of ML models. Therefore, improving data quality and employing the right feature engineering techniques are critical to creating accurate ML models. ML practitioners often tediously iterate on feature engineering, choice of algorithms, and
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Shared by AWS Machine Learning June 9, 2022
Favorite We’re excited to announce that you can now automatically monitor the accuracy of your Amazon Forecast predictors over time. As new data is provided, Forecast automatically computes predictor accuracy metrics, providing you with more information to decide whether to keep using, retrain, or create new predictors. Monitoring predictor quality
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Shared by AWS Machine Learning June 9, 2022
Favorite We are excited to announce that in Amazon Forecast, you can now start your forecast horizon at custom starting points, including on Sundays for weekly forecasts. This allows you to more closely align demand planning forecasts to local business practices and operational requirements. Forecast is a fully managed service
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Shared by AWS Machine Learning June 9, 2022
Favorite This is a guest post by Andrew Degenholtz, CEO and Founder of eMagazines, the parent company of ReadAlong.ai. eMagazines’ technology seamlessly transforms print products into premium digital and audio experiences. Leveraging Amazon technology, ReadAlong.ai offers a simple, turn-key way for publishers to add audio to their websites with a
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Shared by AWS Machine Learning June 9, 2022
Favorite In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). SageMaker JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well
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Shared by AWS Machine Learning June 9, 2022
Favorite Posted by Basil Mustafa, Research Software Engineer and Carlos Riquelme, Research Scientist, Google Research, Brain team Sparse models stand out among the most promising approaches for the future of deep learning. Instead of every part of a model processing every input (“dense” modeling), sparse models employing conditional computation learn
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Shared by Google AI Technology June 9, 2022
Favorite At Google we use technologies like machine learning (ML) to build more useful products — from filtering out email spam, to keeping maps up to date, to offering more relevant search results. Chrome is no exception: We use ML to make web images more accessible to people who are
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Shared by Google AI Technology June 9, 2022
Favorite Data scientists and machine learning (ML) engineers often prepare their data before building ML models. Data preparation typically includes data preprocessing and feature engineering. You preprocess data by transforming data into the right shape and quality for training, and you engineer features by selecting, transforming, and creating variables when
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Shared by AWS Machine Learning June 8, 2022
Favorite In today’s digital landscape, customers are expecting a high-quality experience that is responsive and delightful. Chatbots and virtual assistants have transformed the customer experience from a point-and-click or a drag-and-drop experience to one that is driven by voice or text. You can create a more engaging experience by further
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Shared by AWS Machine Learning June 8, 2022
Favorite Amazon SageMaker comes with two options to spin up fully managed notebooks for exploring data and building machine learning (ML) models. The first option is fast start, collaborative notebooks accessible within Amazon SageMaker Studio – a fully integrated development environment (IDE) for machine learning. You can quickly launch notebooks
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Shared by AWS Machine Learning June 7, 2022
Shared by AWS Machine Learning June 9, 2022
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