AWS Machine Learning Blog

OCTOBER 24, 2024

Conventional ML development cycles take weeks to many months and requires sparse data science understanding and ML development skills. Business analysts’ ideas to use ML models often sit in prolonged backlogs because of data engineering and data science team’s bandwidth and data preparation activities.

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Data Warehouse Machine Learning Machine Learning Cloud Data 103

AWS Machine Learning Blog

AUGUST 1, 2024

Meta Llama3 8B is a gated model on Hugging Face, which means that users must be granted access before they’re allowed to download and customize the model. QLoRA quantizes a pretrained language model to 4 bits and attaches smaller low-rank adapters (LoRA), which are fine-tuned with our training data.

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SQL AWS ML ML 116

phData

OCTOBER 11, 2023

In this blog, we will focus on integrating Power BI within KNIME for enhanced data analytics. KNIME and Power BI: The Power of Integration The data analytics process invariably involves a crucial phase: data preparation. This phase demands meticulous customization to optimize data for analysis.

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Power BI Data Preparation Analytics Data Warehouse 45

AWS Machine Learning Blog

MAY 25, 2023

Dockerfile requirements.txt Create an Amazon Elastic Container Registry (Amazon ECR) repository in us-east-1 and push the container image created by the downloaded Dockerfile. For this solution, we use QuickSight for the business intelligence (BI) dashboard and Athena as the data source for QuickSight.

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ML ML AWS Database 81

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Data Visualization

AWS Machine Learning Blog

NOVEMBER 30, 2023

The publicly available Llama models have been downloaded more than 30M times, and customers love that Amazon Bedrock offers them as part of a managed service where they don’t need to worry about infrastructure or have deep ML expertise on their teams. Amazon Q supports other popular work automation tools like Zendesk and Service Now.

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AWS Machine Learning Blog

NOVEMBER 27, 2024

After you download the code base, you can deploy the project following the instructions outlined in the GitHub repo. Dataset preparation consists of the following key steps: Data acquisition – We begin by downloading a collection of games in PGN format from publicly available PGN files on the PGN mentor program website.

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