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Unlocking generative AI for enterprises: How SnapLogic powers their low-code Agent Creator using Amazon Bedrock

AWS Machine Learning Blog

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. This data was then integrated into Salesforce as a real-time feed of market insights.

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How the UNDP Independent Evaluation Office is using AWS AI/ML services to enhance the use of evaluation to support progress toward the Sustainable Development Goals

AWS Machine Learning Blog

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 94
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Accelerating time-to-insight with MongoDB time series collections and Amazon SageMaker Canvas

AWS Machine Learning Blog

By exploring these challenges, organizations can recognize the importance of real-time forecasting and explore innovative solutions to overcome these hurdles, enabling them to stay competitive, make informed decisions, and thrive in today’s fast-paced business environment. For more information, refer to the following resources.

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Boost your forecast accuracy with time series clustering

AWS Machine Learning Blog

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. Irvine, CA: University of California, School of Information and Computer Science.

Python 100
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Llamaindex Query Pipelines: Quickstart Guide to the Declarative Query API

Towards AI

Image by Narciso on Pixabay Introduction Query Pipelines is a new declarative API to orchestrate simple-to-advanced workflows within LlamaIndex to query over your data. Other frameworks have built similar approaches, an easier way to build LLM workflows over your data like RAG systems, query unstructured data or structured data extraction.

LLM 105
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Build well-architected IDP solutions with a custom lens – Part 1: Operational excellence

AWS Machine Learning Blog

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 115
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List of ETL Tools: Explore the Top ETL Tools for 2025

Pickl AI

It provides insights into considerations for choosing the right tool, ensuring businesses can optimize their data integration processes for better analytics and decision-making. Introduction In todays data-driven world, organizations are overwhelmed with vast amounts of information. What are ETL Tools?

ETL 52