Resources

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
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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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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...

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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...

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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...

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Synthetic data quality metrics PDF report

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...

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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...

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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...

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