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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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...
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...
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...
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...
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...
Generating synthetic versions of simple time series data Time series data is all around us, from health metrics to transaction logs. The increasing proliferation of IoT devices and sensors means that more and more time series data is...
How data improves models by lessening uncertainty It’s not every day that I read an academic paper that does a perfect job of balancing philosophical rigor and technical depth. I love deeply technical and applied ML research that drives...
Exploring TimeGAN and YData Fabric for Synthetic Data Generation of Temporal Patterns In order to accelerate AI development and guarantee the best business practices and results, organizations rapidly need to become more data-centric....
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