Flipboard

MAY 29, 2025

For scalability and search performance, we index the embedding vectors in a vector database. Step 4: Consolidation and vector database integration The final pipeline step consolidates the processed embeddings into a unified dataset and loads them into vector databases optimized for similarity search.

AWS 116

AWS ML ML Machine Learning 116

AWS Machine Learning Blog

NOVEMBER 30, 2023

Nobody else offers this same combination of choice of the best ML chips, super-fast networking, virtualization, and hyper-scale clusters. Customers are telling us that Neuron has made it easy for them to switch their existing model training and inference pipelines to Trainium and Inferentia with just a few lines of code.

AWS 144

AWS AI AI ML 144

ODSC - Open Data Science

SEPTEMBER 29, 2023

A 2019 survey by McKinsey on global data transformation revealed that 30 percent of total time spent by enterprise IT teams was spent on non-value-added tasks related to poor data quality and availability. One way to address this is to implement a data lake: a large and complex database of diverse datasets all stored in their original format.

Data Lakes 52

Data Lakes Clustering Big Data Big Data 52

DrivenData Labs

DECEMBER 10, 2023

or GPT-4 arXiv, OpenAlex, CrossRef, NTRS lgarma Topic clustering and visualization, paper recommendation, saved research collections, keyword extraction GPT-3.5 degree in AI and ML specialization from Gujarat University, earned in 2019. bge-small-en-v1.5 He holds an M.S.

AI 147

AI AI Natural Language Processing Artificial Intelligence 147

AWS Machine Learning Blog

SEPTEMBER 11, 2024

The following figure illustrates the idea of a large cluster of GPUs being used for learning, followed by a smaller number for inference. The Inferentia chip became generally available (GA) in December 2019, followed by Trainium GA in October 2022, and Inferentia2 GA in April 2023.

AWS 108

AWS ML ML Clustering 108

Flipboard

APRIL 13, 2023

To give a sense for the change in scale, the largest pre-trained model in 2019 was 330M parameters. Second, customers want integration into applications to be seamless, without having to manage huge clusters of infrastructure or incur large costs. Today’s FMs, such as the large language models (LLMs) GPT3.5

AWS 182

AWS ML ML AI 182

Cambridge Intelligence

FEBRUARY 20, 2025

GraphViz [Graphviz] has important applications in networking, bioinformatics, software engineering, database and web design, machine learning, and in visual interfaces for other technical domains. Format: Open source automatic graph drawing/design tool that uses a simple graph description language (DOT) for nodes, edges, clusters etc.

Data Visualization 52

Data Visualization Data Analyst Algorithm Clustering 52

Explosion

DECEMBER 14, 2021

Clustering health aspects ? The ICD-11 (International Classification of Diseases) is a database that holds a wide variety of health information about diseases and symptoms. Clustering health aspects Health aspects can have many synonyms or similar contexts such as: ” sore throat ”, ” itchy throat ”, or ” swollen throat ”.

Clustering 52

Clustering Machine Learning Machine Learning Natural Language Processing 52

Hacker News

JANUARY 9, 2024

And in a similar vein, we can expect LLMs to be useful in making connections to external databases, functions, etc. but with things like clustering). We introduced it in 2019 as a way to make specific, individual contributed functions available in the Wolfram Language. But in Version 14.0

Python 181

Python Algorithm Machine Learning Machine Learning 181

Hacker News

NOVEMBER 14, 2023

OnPrem - Geospatial database D2. OnPrem - SAP database D4. OnCloud - Large mirror database D10. OnPrem - LotusNotes database D11. OnPrem - LotusNotes database D11. OnPrem - IBM BPM database D12. In 2000s many of our systems were built on top of IBM Lotus Notes databases. OnPrem - Sharepoint D7.

Power BI 119

Power BI Database Algorithm Azure 119

DrivenData Labs

DECEMBER 7, 2023

Solvers submitted a wide range of methodologies to this end, including using open-source and third party LLMs (GPT, LLaMA), clustering (DBSCAN, K-Means), dimensionality reduction (PCA), topic modeling (LDA, BERT), sentence transformers, semantic search, named entity recognition, and more. and DistilBERT.

Natural Language Processing 130

Natural Language Processing Clustering Data Science Data Analysis 130

AWS Machine Learning Blog

OCTOBER 11, 2024

This post dives deep into Amazon Bedrock Knowledge Bases , which helps with the storage and retrieval of data in vector databases for RAG-based workflows, with the objective to improve large language model (LLM) responses for inference involving an organization’s datasets. The LLM response is passed back to the agent.

Database 106

Database AWS Clustering Data Lakes 106