Electrocardiogram data, one of the most widely available biosignal data, has become increasingly valuable with the emergence of deep learning methods, providing novel insights into cardiovascular diseases and broader health conditions. However, heterogeneity of electrocardiogram formats, limited access to deep learning model weights and intricate algorithmic steps for effective fine-tuning for own disease target labels result in complex workflows. In this work, we introduce ExChanGeAI, a web-based end-to-end platform that streamlines the reading of different formats, pre-processing, visualization and custom machine learning with local and privacy-preserving fine-tuning. ExChanGeAI is adaptable for use on both personal computers and scalable to high performance server environments. The platform offers state-of-the-art deep learning models for training from scratch, alongside our novel open-source electrocardiogram foundation model CardX, pre-trained on over one million electrocardiograms. Evaluation across three external validation sets, including an entirely new testset extracted from routine care, demonstrate the fine-tuning capabilities of ExChanGeAI. CardX outperformed the benchmark foundation model while requiring significantly fewer parameters and lower computational resources. The platform enables users to empirically determine the most suitable model for their specific tasks based on systematic validations.The code is available at https://imigitlab.uni-muenster.de/published/exchangeai .

Natural Language Processing (NLP) has long played a significant role in the compliance processes for major banks around the world. By implementing the different NLP techniques into the production processes, compliance departments can maintain detailed checks and keep up with regulator demands. All of these areas can benefit from document processing and the use of NLP techniques to get through the process more effectively. Certain verification tasks fall beyond the realm of using traditional, rules-based NLP systems. This is where deep learning can help fill these gaps, providing smoother and more efficient compliance checks. There are several challenges that make the rules-based system more complicated to use when undergoing check routines.

Netflix's Korean drama "Squid Game" was one of the most-watched dubbed series of all time, proving the massive potential for foreign-language programming to become a hit in overseas markets. Now, a startup called Deepdub is capitalizing on the growing demand for localized content by automating parts of the dubbing process using AI technology. With its end-to-end platform, Deepdub can decrease the time it takes to complete a dubbing project, allowing content owners and studios to have results in weeks instead of months. What's more, it does this by using just a few minutes of the actors' voices -- so the dubbed version sounds more like the original. The Tel Aviv startup has now closed on $20 million in Series A funding for its efforts, led by New York-based investment firm Insight Partners.

Products developed to manage artificial intelligence data are still largely fragmented, solving one problem at a time for developers, but not the entire life cycle. Enter Sama, a company providing high-quality training data that powers AI technology applications. CEO Wendy Gonzalez said the company is developing the first end-to-end AI tool for training data through machine learning. To do this, the company secured an oversubscribed $70 million in Series B financing led by Caisse de dépôt et placement du Québec (CDPQ), with participation from First Ascent Ventures, Salesforce Ventures, Vistara Growth and all existing investors. The new capital infusion comes two years after the company raised $14.8 million in a Series A round.

Ever since Google has publicised Tensorflow, its application in Deep Learning has been increasing tremendously. It is used even more in research and production for authoring ML algorithms. Though it is flexible, it does not provide an end-to-end production system. On the other hand, Sibyl has end-to-end facilities but lacks flexibility. Google then came up with Tensorflow Extended(TFX) idea as a production-scaled machine learning platform on Tensorflow, taking advantage of both Tensorflow and Sibyl frameworks.

In this regular insideBIGDATA feature we highlight our industry's movers and shakers, companies that are pushing technology forward, and setting trends for innovation. We look at companies with a focus on big data, data science, machine learning, AI and deep learning – some new, some old, always leading, always dynamic. We also take deep dives into new technology promoted (or hyped) as "AI" or my favorite "AI-powered" to provide transparency for what's really going on under the hood. In this installment of "AI Under the Hood" I introduce Kasakda, Inc., a Seattle-based early stage company founded in January 2018. Kaskada is a machine learning platform for feature engineering using event-based data.

Artificial intelligence will come to the forefront this year in marketing departments across the world. But do marketers really understand what AI is in marketing and how to best implement it in their marketing strategies? A Demandbase and Wakefield Research AI survey asked just these kinds of questions of marketers and found some very interesting results. To understand the results of this survey at a deeper level, I spoke with Aman Naimat, SVP of Technology at Demandbase. Naimat has a very extensive background in data science.