MLOps : MLOps, or Machine Learning Operations, is the practice of integrating machine learning models into the software development lifecycle. It involves a collaboration between data scientists, software engineers, and IT operations teams to ensure that machine learning models are efficiently deployed, monitored, and maintained in a production environment. One example of MLOps in action […]

ML-as-a-Service (MLaaS) : MLaaS, or Machine Learning as a Service, is a term used to describe the practice of providing machine learning capabilities through the cloud. This allows organizations to leverage the power of machine learning without the need for expensive hardware and specialized expertise. One example of MLaaS is Amazon Web Services (AWS) which […]

MIS : Management Information Systems, or MIS, is a term used to describe the integration of technology, people, and business processes to manage and analyze data in order to support decision-making and improve organizational efficiency. In today’s digital age, MIS plays a critical role in supporting the operations of businesses, governments, and other organizations. One […]

Mixture transition distribution model : A mixture transition distribution model is a type of statistical model that represents the distribution of a random variable as a mixture of other distributions. In other words, it is a probabilistic model that assumes that the underlying distribution of the random variable is composed of multiple sub-distributions, each with […]

Mixed-effects logistic regression : Mixed-effects logistic regression is a type of regression analysis that allows for the examination of both fixed and random effects within a single model. This type of analysis is useful when studying data that has a hierarchical or nested structure, such as when multiple observations are made within each subject or […]

Misspecification : Misspecification refers to the incorrect specification or construction of a model. In other words, it refers to the situation when the model used to analyze a particular phenomenon or data does not accurately capture the underlying relationships and patterns. This can lead to inaccurate or misleading results and conclusions. One example of misspecification […]

Missing values : Missing values, also known as missing data or missing observations, refer to the lack of information or data for a specific variable in a dataset. This can occur for various reasons, such as when a survey respondent does not answer a question, when a measurement is not taken, or when data is […]

Mis-interpretation of P-values : P-values are a common statistical measure used to determine the likelihood of a given result occurring by chance. A low P-value indicates a strong likelihood that the result is not due to chance, while a high P-value suggests that the result could have occurred by chance. However, there is a tendency […]

Misclassification error : Misclassification error, also known as classification error or error rate, is a common mistake that occurs in the process of classification in data analysis. It refers to the incorrect prediction or assignment of a sample to a class. Misclassification error can have significant consequences in various applications, such as medical diagnosis, credit […]

Mirror-match bootstrapping : Mirror-match bootstrapping is a method used in artificial intelligence (AI) to improve the performance of a machine learning model. It involves creating new training data from existing data by making slight modifications to the original data. For example, imagine a machine learning model that has been trained to recognize different types of […]