Towards AI

FEBRUARY 11, 2025

Beyond Scale: Data Quality for AI Infrastructure The trajectory of AI over the past decade has been driven largely by the scale of data available for training and the ability to process it with increasingly powerful compute & experimental models. Author(s): Richie Bachala Originally published on Towards AI.

Data Quality 56

Data Quality Data Engineering Data Engineering Data Engineer 56

DagsHub

AUGUST 2, 2023

This is how we came up with the Data Engine - an end-to-end solution for creating training-ready datasets and fast experimentation. Let’s explain how the Data Engine helps teams do just that. Preparing and organizing data into a format suitable for training models presents significant challenges for ML teams.

ML 52

ML ML Data Engineer Data Engineering 52

Pickl AI

AUGUST 1, 2024

This capability allows Deep Learning models to excel in tasks such as image and speech recognition, natural language processing, and more. Job Roles and Responsibilities Data Engineering: Defining data requirements, collecting, cleaning, and preprocessing data for training Deep Learning models.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

Snorkel AI

JULY 5, 2023

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. More features mean more data consumed upstream. AR : Yeah.

Supervised Learning 52

Supervised Learning AI AI ML 52

Snorkel AI

JULY 5, 2023

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. More features mean more data consumed upstream. AR : Yeah.

AI 52

AI AI Supervised Learning ML 52

Snorkel AI

JULY 5, 2023

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. More features mean more data consumed upstream. AR : Yeah.

AI 52

AI AI Supervised Learning ML 52

DagsHub

JANUARY 6, 2025

Data Quality and Consistency in Labeling While high data quality and consistent labeling across the dataset are crucial, achieving them can be a little challenging if you do not follow and standardized approach, proper guidelines, and efficient tools and software.

Machine Learning 81

Machine Learning Machine Learning Data Quality Supervised Learning 81