AiVibeMachine Learning (ML)
Machine learning is a branch of artificial intelligence in which systems learn patterns from data to make predictions or decisions, improving with experience rather than following only rules written explicitly by programmers.
A machine learning model is a mathematical function with adjustable parameters. Training feeds it examples and adjusts the parameters to minimise a loss function that measures its errors, after which the trained model is used for inference on new data. The main paradigms are supervised learning from labelled examples, unsupervised learning that finds structure in unlabelled data, and reinforcement learning from rewards. Algorithms range from linear regression, decision trees and gradient-boosted trees to deep neural networks.
In industry, machine learning predicts equipment failures from sensor data, detects defects in images, forecasts demand and energy use, optimises process settings, flags anomalous network traffic and powers language and speech interfaces. Gradient-boosted trees remain strong performers on tabular data, while deep learning dominates images, audio and text.
Results depend on data quality and on whether the training data represents the conditions seen in operation. Models are evaluated on held-out data, monitored for drift after deployment and retrained when needed. Managing this life cycle is the discipline of MLOps, and frameworks such as the NIST AI Risk Management Framework address the governance of AI systems.
Key points
- Models learn parameters from data by minimising a loss function
- The main paradigms are supervised, unsupervised and reinforcement learning
- Gradient-boosted trees suit tabular data; deep learning suits images, audio and text
- Deployed models need monitoring for drift and periodic retraining
Where AiVibe comes in
AiVibe delivers AI and machine learning services, chatbots and virtual assistants with RAG, MCP tools and voice, AI quality management including bias detection and model validation, and the AIMURUGA AI agent, and builds Intel-based edge AI devices using the Intel Distribution of OpenVINO toolkit.
Related terms
- Deep LearningAI & Machine Learning
- Supervised LearningAI & Machine Learning
- Unsupervised LearningAI & Machine Learning
- MLOpsAI & Machine Learning
- Predictive Maintenance (PdM)Quality, Reliability & Maintenance
- Anomaly DetectionAI & Machine Learning