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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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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

Data ingestion and extraction Evaluation reports are prepared and submitted by UNDP program units across the globe—there is no standard report layout template or format. The data ingestion and extraction component ingests and extracts content from these unstructured documents.

ML 72
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First ODSC Europe 2023 Sessions Announced

ODSC - Open Data Science

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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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.

Python 86
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Unfolding the Details of Hive in Hadoop

Pickl AI

Thus, making it easier for analysts and data scientists to leverage their SQL skills for Big Data analysis. It applies the data structure during querying rather than data ingestion. How Data Flows in Hive In Hive, data flows through several steps to enable querying and analysis.

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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 90
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Comparing Tools For Data Processing Pipelines

The MLOps Blog

A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process. Data Ingestion : Involves raw data collection from origin and storage using architectures such as batch, streaming or event-driven.

ETL 59