AWS Machine Learning Blog

DECEMBER 14, 2023

Our pipeline belongs to the general ETL (extract, transform, and load) process family that combines data from multiple sources into a large, central repository. The solution does not require porting the feature extraction code to use PySpark, as required when using AWS Glue as the ETL solution. session.Session().region_name

ETL 100

ETL Data Scientist Machine Learning Deep Learning 100

AWS Machine Learning Blog

SEPTEMBER 1, 2023

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.

Generative AI 122

Generative AI Prompt Engineer Prompt Engineering ML 122

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