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 120

Generative AI Prompt Engineer Prompt Engineering ML 120

Bugra Akyildiz

NOVEMBER 20, 2022

AI for DevOps to infuse AI/ML into the entire software development lifecycle to achieve high productivity. The library is centered on the following concetps: ETL : central framework to create data pipelines. DALL·E Flow is an interactive workflow for generating high-definition images from text prompt. the prompt.

ML 52

ML Data Science ETL DevOps 52

The MLOps Blog

AUGUST 3, 2023

At a high level, we are trying to make machine learning initiatives more human capital efficient by enabling teams to more easily get to production and maintain their model pipelines, ETLs, or workflows. I term it as a feature definition store. How is DAGWorks different from other popular solutions? Stefan: You’re exactly right.

ML 52

ML Data Scientist Software Engineer Machine Learning 52

The MLOps Blog

MARCH 28, 2023

The DevOps and Automation Ops departments are under the infrastructure team. Each time they modify the code, the definition of the pipeline changes. The infrastructure team focuses on technology and delivers tools that other teams will adapt and use to work on their main deliverables. On top of the teams, they also have departments.

Machine Learning 59

Machine Learning Data Scientist Automation ML 59