AWS Machine Learning Blog

MAY 31, 2024

In this blog post and open source project , we show you how you can pre-train a genomics language model, HyenaDNA , using your genomic data in the AWS Cloud. Amazon SageMaker Amazon SageMaker is a fully managed ML service offered by AWS, designed to reduce the time and cost associated with training and tuning ML models at scale.

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AWS Machine Learning Blog

DECEMBER 13, 2024

The approach uses three sequential BERTopic models to generate the final clustering in a hierarchical method. Clustering We use the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) method to form different use case clusters. Lastly, a third layer is used for some of the clusters to create sub-topics.

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AWS Machine Learning Blog

FEBRUARY 25, 2025

The integration with Amazon Bedrock is achieved through the Boto3 Python module, which serves as an interface to the AWS, enabling seamless interaction with Amazon Bedrock and the deployment of the classification model. This doesnt imply that clusters coudnt be highly separable in higher dimensions.

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Data Science Dojo

AUGUST 24, 2023

MLOps practices include cross-validation, training pipeline management, and continuous integration to automatically test and validate model updates. Examples include: Cross-validation techniques for better model evaluation. Managing training pipelines and workflows for a more efficient and streamlined process.

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Pickl AI

JULY 12, 2024

Python facilitates the application of various unsupervised algorithms for clustering and dimensionality reduction. K-Means Clustering K-means partition data points into K clusters based on similarities in feature space.

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AWS Machine Learning Blog

FEBRUARY 10, 2023

Quantitative evaluation We utilize 2018–2020 season data for model training and validation, and 2021 season data for model evaluation. We perform a five-fold cross-validation to select the best model during training, and perform hyperparameter optimization to select the best settings on multiple model architecture and training parameters.

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Pickl AI

NOVEMBER 28, 2024

Key techniques in unsupervised learning include: Clustering (K-means) K-means is a clustering algorithm that groups data points into clusters based on their similarities. Unit testing ensures individual components of the model work as expected, while integration testing validates how those components function together.

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