June 11, 2023

Unlocking the Power of a Data Catalog for Your Business

The importance of data quality & profiling for the success of Machine Learning In today's world, businesses around the globe are generating a vast amount of data. To be able to adopt a data-driven initiative, organizations must manage data...

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Unlocking the Power of a Data Catalog for Your Business
Protecting Your Organization's Data, Synthetic data with Anonymization

Protecting Your Organization's Data: Synthetic data + Anonymization

Attending to the current panorama of privacy regulations such as GRPD and CCPA, synthetic data has become an indispensable strategy for organizations looking to unlock their data sharing and development initiatives. Synthetic data is...

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Fabric vs SDV

Fabric vs SDV: Open-Source or Proprietary Synthetic Data Solution

In the current Data-Centric AI paradigm, where all businesses seek to leverage the power of their data for any competitive advantage they can get, organizations face a critical choice: to buy or build their solutions. The landscape of...

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magnifying glass in computer

Combining Great Expectations with Fabric: Create Better ML datasets

In the fast pace of today’s data-driven world, synthetic data is becoming an important resource of data projects across industries. Automated decision-making systems in healthcare, algorithmic trading, fraud detection, telecommunications,...

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close up pc

Data-Centric AI in Business: Strategies for Leveraging Data

In the last decade, we’ve increasingly focused on model-centric Artificial Intelligence, building ever more flexible machine learning models. However, a new paradigm shift – Data-Centric AI – is currently revolutionizing the industry, as...

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Accelerating AI Development with Synthetic Data: Best Practices

In the rapidly evolving Artificial Intelligence landscape, data quality is the lifeblood that fuels the development of accurate and efficient models. However, accessing and acquiring high-quality, diverse, and labeled data can be quite a...

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Data-Centric AI landscape by YData

The DataPrepOps Landscape

Since Andrew Ng coined the term in 2021, the number of companies that identify themselves as providing data-centric AI tools has exploded. From synthetic data to data monitoring, companies all over the machine learning workflow have jumped...

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DataPrepOps in the Data-Centric AI context

Coined by Andrew Ng in 2021, the concept of “Data-Centric AI” has taken both academia and industry by storm. It has given rise to hundreds of research publications, fostered the creation of special tracks and colloquiums in the most...

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Correlation Matrix for Multivariate Data

How to Profile Datasets with a big number of Variables?

As the Data-Centric AI paradigm has come to prove that focusing on data quality will have the most transformative impact in industries across all verticals, more and more companies and organizations worldwide are starting to look for the...

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The Future of AI: Data Dominance in an Era of Advanced Models

In recent years, the field of Artificial Intelligence (AI) has witnessed unprecedented advancements, driven by the emergence of Large Language Models (LLMs) and groundbreaking model architectures. These achievements have propelled AI into...

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Synthetic Data helps mitigate data issues in healthcare.

The Role of Synthetic Data in Healthcare: From Innovation to Diagnosis

In our previous article, we discussed how healthcare data is often affected by important data quality issues, creating a challenging context for AI development. These issues comprise imbalance data, missing data, small data, noisy data,...

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Synthetic data offers a multitude of benefits for businesses.

Top 5 Benefits of Synthetic Data in Modern AI

In real-world applications, where data is subjected to a multitude of data quality issues, the implementation of Data-Centric AI best practices becomes severely compromised, which impacts the development of robust AI solutions and...

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Automated process in a healthcare laboratory.

Data-Centric AI in Healthcare: Revolutionizing Diagnosis and Treatment

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 systems. However, the landscape of medical data...

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