One of the primary advantages of using a graph database is the ability to present the relationships that exist between datasets and files. Much of the data is connected, and graph database use cases are increasingly helping to find and explore these relationships and develop new conclusions. Additionally, graph databases are designed for quick data […]
A Brief History of Semantics
As a word, “semantics” was first used by Michel Bréal, a French philologist (a language historian),in 1883. He studied how languages are organized, how languages change as time passes, and the connections within languages. Gen erally speaking, semantics is the study of language and its meaning. More specifically, semantics can be used to describe how […]
Integrating Edge AI
Integrating edge artificial intelligence (AI) is not a simple process. Early forms of artificial intelligence relied on the computer power of data centers to perform their processor-demanding tasks. After some time, AI shifted into software, using predictive algorithms that changed how these systems support businesses. AI has now moved to the outer edges of networks. […]
Data Science vs. Decision Science: A New Era Dawns
Data Science vs. Decision Science: Basic Descriptions In Data Science, a variety of advanced technologies like data mining, statistics, predictive analytics, AI, and machine learning are used in conjunction to deliver solutions for business problems. In Decision Science, analyzed data is “interpreted” to arrive at business decisions that meet specific objectives. So while Data Science […]
The Intersection of Self-Service Analytics and Machine Learning
The terms “self-service analytics” (SSA) and “machine learning” (ML) are frequently used interchangeably, but the concepts behind these terms are a world apart. In self-service analytics, specific tools are designed to aid the user in inputting data or interpreting results (output). On the other hand, a machine learning algorithm is a special software that has […]
A Brief History of Machine Learning
Machine learning (ML) is an important tool for the goal of leveraging technologies around artificial intelligence. Because of its learning and decision-making abilities, machine learning is often referred to as AI, though, in reality, it is a subdivision of AI. Until the late 1970s, it was a part of AI’s evolution. Then, it branched off […]
Machine Learning Transformed: Data Quality and Operational Necessities
Machine learning elicits mixed reactions. On the one hand, some consider machine learning a company’s new super power that has “swept enterprise technology, using mass amounts of data and algorithms to make predictions.” At the same time machine learning has been considered an overhyped fad and a panacea, failing to deliver. While both can be […]
Deep Learning Demystified
The “deep” in deep learning refers to the number of hidden layers involved in the design. Deep learning is a way of training artificial intelligence (AI) to recognize specific data, such as speech or faces, and to make predictions based on previous experiences. Unlike machine learning, which organizes and sends data through predefined algorithms, deep […]
The Future of Business Transformation: No-Code Infrastructure and the Cloud
Click to learn more about author Borya Shakhnovich. In 2020, countless industries were forced to accelerate their digital transformation practices, having no other option but to be agile in supporting the new normal: a remote business infrastructure. What has helped lessen the burden for employers? Implementing no-code infrastructures across business practices and processes to empower […]
So You Want to be a Machine Learning Engineer?
Ideally, a machine learning engineer would have both the skills of a software engineer and the experience of a data scientist and data engineer. However, data scientists and software engineers usually come from very different backgrounds, and data scientists should not be expected to be great programmers, nor should software engineers be expected to provide […]