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Automated process in a healthcare laboratory.

Data-Centric AI in Healthcare: Revolutionizing Diagnosis and Treatment

Cover Photo by Testalize.me on Unsplash In healthcare domains, the collection and exploration of biomedical and clinical data is pivotal to making informed decisions about patient care and developing accurate medical recommendation...

Synthetic Data resembles the creation of an artificial

Generative AI for Tabular Data

Data is the foundation of modern machine learning models. However, data privacy issues, high costs, and the difficulty in obtaining large datasets make it challenging to develop robust and efficient models. This is where synthetic data...

LLMs Impact Data Science Projects

How Large Language Models Impact Data Science Projects

The advent of Large Language Models (LLMs) is undeniably leaving its mark across several fields and industries. In the realm of Data Science, they can also prove extremely transformative in the way technical teams manage and analyze their...

Building a Multi-Document Language Model App

Building a Multi-Document Language Model App

If you haven’t heard it yet, here’s the latest news: the Data-Centric AI Community is organizing regular collaborative coding sessions, called “Code with Me”. In our most recent session, we delved into the exciting world of Large Language...

Synthetic Data Generation

How to evaluate synthetic data quality?

Generating synthetic data lays a crucial role in addressing the problematic aspects of data in Data Science, such as balancing classes, expanding small datasets, and securely sharing sensitive information like bank transactions while...

Differential privacy and privacy controls for synthetic data generation

Differential Privacy: Synthetic data privacy controls

In today's data-driven world, privacy concerns have become paramount. The use of personal data in various applications raises ethical and legal questions, prompting the need for privacy-preserving techniques. Differential privacy has...

Understanding Missing Data Mechanisms

Understanding Missing Data Mechanisms: Types and Implications

Missing data is a common challenge in data quality and can occur for various “reasons”, called “missing data mechanisms”. It is crucial to understand the underlying mechanisms causing missing data as they can significantly impact the...

Explaining Missing Data, DCAI

What is Missing Data in Machine Learning?

Just like when assembling a puzzle, working with missing pieces – i.e., missing data – can compromise our ability to fully understand our datasets. Missing data is just one problem in the wide range of data quality issues that can affect...

Conditional Synthetic Data Generation

Conditional Synthetic Data Generation for Robust Machine Learning

In today's data-driven world, synthetic data has emerged as a valuable asset for organizations across various industries, from telecommunications, transportation, finance, e-commerce, and healthcare. While leveraging realistic and...

Data Quality for Large Language Models

The importance of Data Quality for Large Language Models

Over the past months, Large Language Models (LLMs) have increasingly received a lot of attention both from the general public and research organizations, as well as organizations worldwide, irrespective of their size. In essence, LLMs are...

Integrating YData Fabric and Vertex AI

Integrating YData Fabric and Vertex AI

As proven time and again, data quality is key for high-performance results, which means that in order to extract real value out of their ML efforts, organizations need to incorporate data-centric solutions into their machine-learning...

complex time series

Synthetic Multivariate Time Series Data

Generating synthetic versions of complex time series data As we saw in our previous post, YData Fabric’s time series synthesizer works well for univariate, single-entity datasets, regardless of how complex the processes generating those...

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