How AI assists banks and financial institutions in enhancing financial inclusivity The advancement of technology has paved the way for banks and financial institutions to reach out to all corners of the Earth. However, according to the...
YData was highlighted in the recent report “Emerging Tech Impact Radar: Artificial Intelligence” by Gartner. The world-renowned technology consulting company named YData as a key vendor with a platform that can generate synthetic data to...
ydata-profiling time-series exploratory analysis One of the early steps in the data science development cycle is to understand and explore the data for the problem you’re solving. EDA is a crucial step for a better data science workflow,...
Data-Centric AI is here to stay, and experts will tell you why. If you are working in the AI / ML industry in 2022, there is no way you have not heard about the Data-Centric AI idea: introduced recently by Andrew Ng, this approach implies...
Leverage EDA to beat GANs challenges Machine Learning has mainly evolved as a model-centric practice where achieving quality results is left pending small data processing efforts and instead concentrated on experimenting with families of...
Digging beyond the standard data profiling Pandas Profiling was always my goto-secret tool to understand the data and uncover meaningful insights, in a few minutes, under a few lines of code. Whenever I was given a new dataset, I would...
A practical guide to generating synthetic data using open-sourced GAN implementations The advancements in technology have paved the way for generating millions of gigabytes of real-world data in a single minute, which would be great for...
Data-centric AI and the reshape of the tooling space The end-to-end development of Data Science solutions can be broadly described as the process of analysis, planning, development and operationalization of a business problem that can be...
A guide to go a step beyond with your data Lately, there has been a lot of discussion about data quality and its impacts on model performance. Mainly due to this presentation which highlighted this topic — model-centric vs data-centric,...
A place to discuss data quality for data science According to Alation’s State of Data Culture Report, 87% of employees attribute poor data quality to why most organizations fail to adopt AI meaningfully. Based on a 2020 study by McKinsey,...
A tutorial on how you can combine ydata-synthetic with Great Expectations With the rapid evolution of machine learning algorithms and coding frameworks, the lack of high-quality data is the real bottleneck in the AI industry. Transform...
The answer is pretty obvious, right? Let’s take a deeper look at the P&C business. Like any other business nowadays, artificial intelligence also became a vital aspect of modern Insurance. Insurance companies seat on a gold mine of data,...
