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Initial (chaotic, ad hoc, individual heroics) - the starting point for use of a new or undocumented repeat process. Repeatable - the process is at least documented sufficiently such that repeating the same steps may be attempted. Defined - the process is defined/confirmed as a standard business process
CMMI defines the following five maturity levels (1 to 5) for processes: Initial, Managed, Defined, Quantitatively Managed, and Optimizing. CMMI Version 3.0 was published in 2023; [ 1 ] Version 2.0 was published in 2018; Version 1.3 was published in 2010, and is the reference model for the rest of the information in this article.
The implementation maturity model (IMM) is an instrument to help an organization in assessing and determining the degree of maturity of its implementation processes. This model consists of two important components, namely the: five maturity levels, adopted from capability maturity model (CMM) of the Software Engineering Institute (SEI).
In computational learning theory, probably approximately correct (PAC) learning is a framework for mathematical analysis of machine learning. It was proposed in 1984 by Leslie Valiant . [ 1 ]
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 ...
Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. [1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". [ 2 ]
This can affect the model's understanding and generation capabilities, particularly for languages with rich morphology or tokens not well-represented in the training data. Simplicity in Preprocessing : It simplifies the preprocessing pipeline by eliminating the need for complex tokenization and vocabulary management, reducing the preprocessing ...
Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. This can result in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training the models separately.