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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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...
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...
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,...
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...
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...
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...
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...
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...
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...
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...
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...
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...