The MLOps Blog

MARCH 15, 2023

.” Hence the very first thing to do is to make sure that the data being used is of high quality and that any errors or anomalies are detected and corrected before proceeding with ETL and data sourcing. If you aren’t aware already, let’s introduce the concept of ETL. We primarily used ETL services offered by AWS.

ETL 52

ETL Data Drift Machine Learning ML 52

Mlearning.ai

FEBRUARY 9, 2023

While dealing with larger quantities of data, you will likely be working with Data Engineers to create ETL (extract, transform, load) pipelines to get data from new sources. Data Science is an umbrella role with common roles such as Data Analytics, research, ML model building, ML Ops, and ML engineering underneath.

Data Scientist 52

Data Scientist Data Science ML Machine Learning 52

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 123

Generative AI Prompt Engineer Prompt Engineering AI 123

The MLOps Blog

AUGUST 3, 2023

This is Piotr Niedźwiedź and Aurimas Griciūnas from neptune.ai , and you’re listening to ML Platform Podcast. Stefan is a software engineer, data scientist, and has been doing work as an ML engineer. I term it as a feature definition store. I also recently found out, you are the CEO of DAGWorks.

ML 52

ML Data Scientist Software Engineer Machine Learning 52

The MLOps Blog

MARCH 28, 2023

Each time they modify the code, the definition of the pipeline changes. Regarding other teams, they may approach testing ML models differently, especially in tabular ML use cases, by testing on sub-populations of the data. They have production and training code bases in GitHub repositories.

Machine Learning 59

Machine Learning Automation Data Scientist ML 59