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

Incremental training with Amazon SageMaker JumpStart

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

Easily create and store features in Amazon SageMaker without code

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

Integrate Amazon Lex and Uneeq’s digital human platform

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

Amazon SageMaker Studio and SageMaker Notebook Instance now come with JupyterLab 3 notebooks to boost developer productivity

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

How InfoJobs (Adevinta) improves NLP model prediction performance with AWS Inferentia and Amazon SageMaker

Favorite This is a guest post co-written by Juan Francisco Fernandez, ML Engineer in Adevinta Spain, and AWS AI/ML Specialist Solutions Architects Antonio Rodriguez and João Moura. InfoJobs, a subsidiary company of the Adevinta group, provides the perfect match between candidates looking for their next job position and employers looking

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Shared by AWS Machine Learning June 7, 2022