Create, train, and deploy a billion-parameter language model on terabytes of data with TensorFlow and Amazon SageMaker

Favorite The increasing size of language models has been one of the biggest trends in natural language processing (NLP) in recent years. Since 2018, we’ve seen unprecedented development and deployment of ever-larger language models, including BERT and its variants, GPT-2, T-NLG, and GPT-3 (175 billion parameters). These models have pushed

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Shared by AWS Machine Learning June 14, 2022

Enable business analysts to access Amazon SageMaker Canvas without using the AWS Management Console with AWS SSO

Favorite IT has evolved in recent years: thanks to low-code and no-code (LCNC) technologies, an increasing number of people with varying backgrounds require access to tools and platforms that were previously a prerogative to more tech-savvy individuals in the company, such as engineers or developers. Out of those LCNC technologies,

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Shared by AWS Machine Learning June 14, 2022

Unified data preparation and model training with Amazon SageMaker Data Wrangler and Amazon SageMaker Autopilot

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

Continuously monitor predictor accuracy with Amazon Forecast

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