Artificial intelligence
Machine Learning:
Extracting Knowledge from Observation Series
Artificial intelligence algorithms surpass traditional approaches because they can autonomously interpret operational scenarios and phenomena of interest, without requiring detailed descriptions as in model-based methods. Machine learning extracts knowledge from data from various sources, such as sensors, automatically training a model that addresses specific problems. This technology does not require explicit programming or complex modeling, making it highly beneficial for business value, as it simulates human learning capabilities.
Application domain study
Analyze the state of the art and the various solutions available in the literature.
Targeted strategy
Determine goals and define a success criterion to achieve results.
Data acquisition
Define the different data acquisition campaigns.
Creating the model
Determining the machine learning algorithms to use for training.
Model validation
The execution process may include many cycles of running the routine to optimize and refine the results.
Data processing
Identify how to prepare and process data for machine learning.
USE CASE:
AIVisionPark
Smart solutions for Urban mobility