YouTube is the second-largest social media network and search engine on the planet, with over 2.3 billion active users.  Since its inception in 2005, YouTube has grown into a tech behemoth. It is now a hub for hobby content creators, professional influencers, and social media marketing specialists.  Over 12 million

For decades, managing data essentially meant collecting, storing, and occasionally accessing it. That has all changed in recent years, as businesses look for the critical information they can pull from the massive amounts of data generated, accessed, and stored in myriad locations, from corporate data centers to the cloud and

Google today released TensorFlow Graph Neural Networks (TF-GNN) in alpha, a library designed to make it easier to work with graph-structured data using TensorFlow, its machine learning framework. Used in production at Google for spam and anomaly detection, traffic estimation, and YouTube content labeling, Google says that TF-GNN is designed to “encourage

The world has come to rely on data. Data-driven analytics fuel marketing strategies, supply chain operations, and more, and often to impressive results. However, without careful attention to data accuracy, these analytics can steer businesses in the wrong direction. Just as data analytics can be detrimental if not executed properly,

Our world revolves around data. Data science, machine learning, and AI have become some of the most critical technologies and industries in the 21st Century, penetrating almost every aspect of our lives, including fitness and wellbeing. The impact of technology on fitness industry On the consumer side, this technology triumvirate

Big data is one of the most rapidly growing industries in the world and was valued at $169 billion in 2018, with expectations to approach the $300 billion mark by the end of next year. Even with such monetary influence in the world already, the industry is still figuring itself

There’s been an ongoing trend of people wanting increasingly capable and smaller tech devices. Those desires have spurred progress in a segment of artificial intelligence (AI) called TinyML. Here’s a look at how it could enhance future possibilities.  What Is TinyML? It’s already widely known that processing data directly on

Machine learning (ML) is how a system learns and adapts its processes from the patterns found in large amounts of data. When we think of machine learning, some prominent examples come to mind. For instance, the way product recommendations on Amazon are eerily similar to Google searches you’ve done. The

This article was originally published on Grit Daily and is reproduced with permission. The financial crisis of 2007 – 2008 resulted in marked changes to the institutional financial sector landscape. Weaknesses were brought to light and financial institutions were forced to reevaluate how they were managing data and restructure as

Data science is now essential to e-commerce success. Targeting the right audience through advertising platforms is highly necessary to boost online sales as customers only want to look at relevant products or items they need. Artificial intelligence (AI), with the assistance of machine learning (ML), helps determine the target audience