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