What is ML?

ML (Machine Learning) is a field of artificial intelligence in which mathematical models are trained on data to identify patterns, generate predictions, or make decisions without explicit programming of each rule. In practical implementation, ML encompasses supervised, unsupervised, and reinforcement learning, as well as feature engineering, training datasets, loss functions, and model validation procedures. Typical algorithms include linear models, decision trees, ensemble methods, and neural networks, selected based on data structure and requirements for accuracy and interpretability.

In applied scenarios, ML is used for classification, forecasting, anomaly detection, recommendation systems, text analysis, computer vision, and automation of analytical processes. Industrial deployment of machine learning requires well-prepared data, computational infrastructure, accuracy control, monitoring of model degradation, and lifecycle management of solutions in production environments. Additionally, MLOps practices are applied, including automation of training, versioning of models and data, continuous integration and delivery (CI/CD) of models, as well as resource and cost management in cloud or hybrid infrastructures.