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).

ML 80

ML Metadata Data Ingestion Data Extraction 80

AWS Machine Learning Blog

DECEMBER 18, 2023

MongoDB Atlas offers automatic sharding, horizontal scalability, and flexible indexing for high-volume data ingestion. Among all, the native time series capabilities is a standout feature, making it ideal for a managing high volume of time-series data, such as business critical application data, telemetry, server logs and more.

Data Extraction 120

Data Extraction Data Ingestion ML Machine Learning 120

AWS Machine Learning Blog

APRIL 4, 2023

For an example of clustering based on this metric, refer to Cluster time series data for use with Amazon Forecast. In this post, we generate features from the time series dataset using the TSFresh Python library for data extraction. Egor Miasnikov is a Solutions Architect at AWS based in Germany.

Python 94

Python Machine Learning Explainability Data Ingestion 94

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.

Machine Learning 98

Machine Learning Data Science Deep Learning Data Ingestion 98

AWS Machine Learning Blog

NOVEMBER 22, 2023

It is crucial to pursue a metrics-driven strategy that emphasizes the quality of data extraction at the field level, particularly for high-impact fields. Harness a flywheel approach, wherein continuous data feedback is utilized to routinely orchestrate and evaluate enhancements to your models and processes.

IDP 103

IDP Machine Learning Data Extraction ML 103

Pickl AI

OCTOBER 17, 2024

Summary: The ETL process, which consists of data extraction, transformation, and loading, is vital for effective data management. Following best practices and using suitable tools enhances data integrity and quality, supporting informed decision-making. Introduction The ETL process is crucial in modern data management.

ETL 52

ETL Explainability Data Integration Data Extraction 52

AWS Machine Learning Blog

OCTOBER 23, 2024

Data flow Here is an example of this data flow for an Agent Creator pipeline that involves data ingestion, preprocessing, and vectorization using Chunker and Embedding Snaps. The resulting vectors are stored in OpenSearch Service databases for efficient retrieval and querying. The next paragraphs illustrate just a few.

Generative AI 75

Generative AI IDP LLM Automation 75