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Artificial intelligence engineering (AI engineering) is a technical discipline that focuses on the design, development, and deployment of AI systems. AI engineering involves applying engineering principles and methodologies to create scalable, efficient, and reliable AI-based solutions.
Mark Zuckerberg is spending more than ever at Meta. The billionaire has announced plans to build a new data center and an "AI engineer." Mark Zuckerberg has announced a new spending plan for Meta ...
Salary trends from Levels.fyi, an online platform that allows tech workers to compare their compensation packages, found that in the past two years, entry-level AI engineers can earn 8% more than ...
Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases).
Data science involves the application of machine learning to extract knowledge from data. Subfields of machine learning include deep learning, supervised learning, unsupervised learning, reinforcement learning, semi-supervised learning, and active learning. Causal inference is another related component of information engineering.
Data engineering refers to the building of systems to enable the collection and usage of data. This data is usually used to enable subsequent analysis and data science, which often involves machine learning. [1] [2] Making the data usable usually involves substantial compute and storage, as well as data processing.
Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession. [4] Data science is "a concept to unify statistics, data analysis, informatics, and their related methods" to "understand and analyze actual phenomena" with data. [5]
Artificial Intelligence (AI) engineering is the interdisciplinary field focused on designing, developing, and deploying AI systems. It combines principles from data and software engineering to create robust, scalable, and efficient solutions for complex tasks.