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Data scientists often work with unstructured data such as text or images and use machine learning algorithms to build predictive models and make data-driven decisions. In addition to statistical analysis , data science often involves tasks such as data preprocessing , feature engineering , and model selection.
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 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.
Ilya Sutskever FRS (born 8 December 1986) is a Canadian-Israeli-Russian computer scientist who specializes in machine learning. [6] Sutskever has made several major contributions to the field of deep learning. [7] [8] [9] He is notably the co-inventor, with Alex Krizhevsky and Geoffrey Hinton, of AlexNet, a convolutional neural network. [10]
She served as an advisor for Deep Learning Indaba, a non-profit which looks to train African people in machine learning. In 2017 she was selected by Forbes magazine as one of 20+ "leading women" in artificial intelligence. [18] Thomas has also written on the application of data science and machine learning in medicine.
MLOps is the set of practices at the intersection of Machine Learning, DevOps and Data Engineering. MLOps or ML Ops is a paradigm that aims to deploy and maintain machine learning models in production reliably and efficiently. The word is a compound of "machine learning" and the continuous delivery practice (CI/CD) of DevOps in the software ...