AWS Machine Learning Blog

FEBRUARY 21, 2025

Previously, he was a Data & Machine Learning Engineer at AWS, where he worked closely with customers to develop enterprise-scale data infrastructure, including data lakes, analytics dashboards, and ETL pipelines.

LLM 111

LLM Large Language Models Natural Language Processing Machine Learning 111

AWS Machine Learning Blog

FEBRUARY 2, 2024

Embeddings capture the information content in bodies of text, allowing natural language processing (NLP) models to work with language in a numeric form. Embeddings are just vectors of floating point numbers, so we can analyze them to help answer three important questions: Is our reference data changing over time?

ETL 111

ETL DevOps LLM Generative AI 111

Marktechpost

JULY 17, 2023

A few automated and enhanced features for feature engineering, model selection and parameter tuning, natural language processing, and semantic analysis are noteworthy. Panoply Panoply is a cloud-based, intelligent end-to-end data management system that streamlines data from source to analysis without using ETL.

Machine Learning 98

Machine Learning Data Mining Data Scientist Data Science 98

Artificial Corner

JUNE 6, 2023

Timeline of data engineering — Created by the author using canva In this post, I will cover everything from the early days of data storage and relational databases to the emergence of big data, NoSQL databases, and distributed computing frameworks. MongoDB, developed by MongoDB Inc.,

Data Mining 52

Data Mining Big Data ETL Machine Learning 52

AWS Machine Learning Blog

APRIL 24, 2023

Amazon Comprehend is a fully managed and continuously trained natural language processing (NLP) service that can extract insight about the content of a document or text. Amazon Comprehend training workflow To start the training the Amazon Comprehend model, we need to prepare the training data.

ML 70

ML NLP Machine Learning ETL 70

Mlearning.ai

FEBRUARY 9, 2023

During my MS, I got the opportunity to work on many types of data and ML projects, including web scraping to collect data, parsing big data, building unsupervised ML models, building supervised ML models, creating deep neural networks, working with text data using Natural Language Processing, and with speech data using audio processing techniques.

Data Scientist 52

Data Scientist Data Science ML Machine Learning 52

AWS Machine Learning Blog

AUGUST 2, 2024

About the authors Vicente Cruz Mínguez is the Head of Data & Advanced Analytics at Cepsa Química. He has more than 8 years of experience with big data and machine learning projects in financial, retail, energy, and chemical industries. His main interests include natural language processing and generative AI.

Generative AI 111

Generative AI LLM Machine Learning Data Scientist 111