Smart Data Collective

OCTOBER 20, 2021

For people striving to rule the data integration and data management world, it should not be a surprise that companies are facing difficulty in accessing and integrating data across system or application data silos. Next-gen technologies such as AI and ML are acting as catalysts for change.

ML 133

ML ML Big Data Big Data 133

ODSC - Open Data Science

APRIL 28, 2023

Be sure to check out her talk, “ Power trusted AI/ML Outcomes with Data Integrity ,” there! Due to the tsunami of data available to organizations today, artificial intelligence (AI) and machine learning (ML) are increasingly important to businesses seeking competitive advantage through digital transformation.

ML 98

ML ML Data Silos Data Quality 98

ML @ CMU

MAY 12, 2023

Unfortunately, while this data contains a wealth of useful information for disease forecasting, the data itself may be highly sensitive and stored in disparate locations (e.g., In this post we discuss our research on federated learning , which aims to tackle this challenge by performing decentralized learning across private data silos.

Data Silos 130

Data Silos Algorithm ML ML 130

phData

MAY 13, 2024

Difficulty in moving non-SAP data into SAP for analytics which encourages data silos and shadow IT practices as business users search for ways to extract the data (which has data governance implications). Additionally, change data markers are not available for many of these tables.

Database 52

Database Analytics Analytics Machine Learning 52

phData

AUGUST 11, 2023

Data engineering in healthcare is taking a giant leap forward with rapid industrial development. Artificial Intelligence (AI) and Machine Learning (ML) are buzzwords these days with developments of Chat-GPT, Bard, and Bing AI, among others. However, data collection and analysis have been commonplace in the healthcare sector for ages.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Heartbeat

DECEMBER 7, 2023

Data should be designed to be easily accessed, discovered, and consumed by other teams or users without requiring significant support or intervention from the team that created it. Data should be created using standardized data models, definitions, and quality requirements. How does it?

Data Governance 52

Data Governance Deep Learning Deep Learning Artificial Intelligence 52

DrivenData Labs

MARCH 30, 2023

His interests are in privacy-preserving machine learning, particularly in the areas of differential privacy, ML security, and federated learning. A noise vector is additionally added to the data, model, loss function & optimizer by using DP-SGD, which defends against privacy inference attacks while maintaining computational resourcing.

Machine Learning 162

Machine Learning Machine Learning Data Science Algorithm 162