AWS Machine Learning Blog

MARCH 29, 2023

In this post, we discuss how the IEO developed UNDP’s artificial intelligence and machine learning (ML) platform—named Artificial Intelligence for Development Analytics (AIDA)— in collaboration with AWS, UNDP’s Information and Technology Management Team (UNDP ITM), and the United Nations International Computing Centre (UNICC).

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ML Metadata Data Ingestion Data Extraction 69

AWS Machine Learning Blog

JUNE 22, 2023

Utilizing data streamed through LnW Connect, L&W aims to create better gaming experience for their end-users as well as bring more value to their casino customers. Predictive maintenance is a common ML use case for businesses with physical equipment or machinery assets.

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Neural Network Metadata ML Machine Learning 82

ODSC - Open Data Science

MARCH 27, 2023

Learn about the flow, difficulties, and tools for performing ML clustering at scale Ori Nakar | Principal Engineer, Threat Research | Imperva Given that there are billions of daily botnet attacks from millions of different IPs, the most difficult challenge of botnet detection is choosing the most relevant data.

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Machine Learning Data Ingestion Explainability ML 98

AWS Machine Learning Blog

MARCH 26, 2024

The traditional approach of using human reviewers to extract the data is time-consuming, error-prone, and not scalable. In this post, we show how to automate the accounts payable process using Amazon Textract for data extraction. You can visualize the indexed metadata using OpenSearch Dashboards.

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IDP Metadata Data Extraction Automation 90

AWS Machine Learning Blog

APRIL 4, 2023

AWS provides various services catered to time series data that are low code/no code, which both machine learning (ML) and non-ML practitioners can use for building ML solutions. We recommend running this notebook on Amazon SageMaker Studio , a web-based, integrated development environment (IDE) for ML.

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Python Explainability Data Ingestion Machine Learning 81

Flipboard

OCTOBER 25, 2023

After decades of digitizing everything in your enterprise, you may have an enormous amount of data, but with dormant value. However, with the help of AI and machine learning (ML), new software tools are now available to unearth the value of unstructured data. The solution integrates data in three tiers.

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ML Metadata AI AI 132

Marktechpost

JULY 14, 2023

Experiment tracking in machine learning is the practice of preserving all pertinent data for each experiment you conduct. Experiment tracking is implemented by ML teams in a variety of ways, including using spreadsheets, GitHub, or in-house platforms. Major ML and DL libraries like TensorFlow, Keras, or Scikit-learn are also supported.

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Machine Learning ML Data Scientist Metadata 52