Marktechpost

JULY 19, 2023

Understanding patterns of model output for subgroups or slices of input data goes beyond examining aggregate metrics like accuracy or F1 score. Stakeholders such as ML engineers, designers, and domain experts must work together to identify a model’s expected and potential faults.

Machine Learning 52

Machine Learning ML Python Metadata 52

The MLOps Blog

MAY 17, 2023

From data processing to quick insights, robust pipelines are a must for any ML system. Often the Data Team, comprising Data and ML Engineers , needs to build this infrastructure, and this experience can be painful. However, efficient use of ETL pipelines in ML can help make their life much easier.

ETL 59

ETL ML Machine Learning Data Scientist 59

Snorkel AI

JULY 5, 2023

Generally, data is produced by one team, and then for that to be discoverable and useful for another team, it can be a daunting task for most organizations. Even larger, more established organizations struggle with data discovery and usage. So does that mean feature selection is no longer necessary?

Large Language Models 52

Large Language Models Metadata AI AI 52

Snorkel AI

JULY 5, 2023

Generally, data is produced by one team, and then for that to be discoverable and useful for another team, it can be a daunting task for most organizations. Even larger, more established organizations struggle with data discovery and usage. So does that mean feature selection is no longer necessary?

Large Language Models 52

Large Language Models Metadata AI AI 52