14 posts tagged with "Machine learning"

AI inference is the process through which an artificial intelligence model applies what it has learned during training to make predictions, classifications or decisions based on new input data. Unlike training, which is computationally heavy, takes hours or days and runs in data centers, inference is lighter, takes milliseconds and runs in the cloud or on edge devices such as smartphones and IoT sensors.

Artificial intelligence (AI) is the broadest field: any technology that lets machines simulate human intelligence. Machine learning (ML) is a subset of AI in which algorithms learn from data without being explicitly programmed, large language models (LLMs) are AI models trained on huge amounts of text to understand and generate natural language, and generative AI is the branch of AI that creates original content such as text, images, music or video.

IoT and machine learning help an agricultural company save water, energy and money: soil moisture sensors allow irrigation only when needed, temperature and humidity sensors cut unnecessary heating and ventilation, and ML models support weather forecasting, crop management and production planning. For a medium-sized agricultural company, Mative's forecast analysis after one year points to 30% less water consumption, 25% lower energy costs, 20% more production and 15% savings in operating costs.

IoT and machine learning bring energy and economic savings to a construction company by monitoring machinery consumption, site climate and construction processes in real time, predicting equipment failures and improving the planning of resources, projects and supply chain. For a construction company, Mative's forecast analysis one year after adopting its IoT and ML solutions estimates 20% lower energy consumption, 15% lower operating costs, a 10% improvement in project quality and 10% savings on maintenance costs.

IoT and machine learning bring energy and economic savings to a manufacturing company by monitoring machinery and facilities in real time, enabling predictive maintenance and optimizing production schedules and processes. For a window, door and frame manufacturer, Mative's forecast analysis one year after adopting its IoT and ML solutions estimates 20% lower energy consumption, 15% lower operating costs, 25% higher production efficiency and 10% savings on maintenance costs.

In a public transport company, IoT sensors monitor fuel consumption, vehicle conditions, traffic and routes, and Machine Learning analyzes these data to forecast consumption, optimize routes and schedule maintenance. Mative's forecast for a company adopting its IoT and ML solutions estimates, after one year, 15% less fuel consumption, 20% lower operating costs, 25% better service punctuality and 10% savings on maintenance costs.

Predictive algorithms are machine learning methods that estimate values from existing data, and Mative applies four of them to its time series. Linear regression and its OLS form model the relationship between a target variable and its predictors, ARIMA predicts future values from past values and past errors, and the Fourier transform reveals cycles and periodic patterns in a signal.

Warning systems in agriculture monitor environmental conditions, plant diseases, pests and adverse weather, and alert farmers in time to make informed decisions, improve productivity and reduce losses. They are typically built on weather data APIs such as OpenWeatherMap, machine learning models that predict the spread of plant diseases, and Agricultural Decision Support Systems (ADSS) that turn field data into recommendations.

In the automotive sector, machine learning helps manage connected vehicle fleets: algorithms learn to detect significant events, monitor vehicle conditions in real time to predict failures before they occur, and forecast demand to optimize the distribution of shared vehicles. The result is less downtime, lower repair costs, shorter waiting times for users and more efficient operations.