Traceability in the context of artificial intelligence and machine learning refers to the ability to follow the training process of a model and track its performance and results at different stages of the process.
Traceability is important in machine learning because it allows model developers and users to understand how the model has been created, what data has been used to train it, what pre-processing techniques have been applied to the data, what machine learning algorithms have been used and how its performance has been evaluated.
Traceability is also important to ensure transparency and accountability in the development and implementation of machine learning models, especially in sensitive areas such as critical decision-making in healthcare, criminal justice and public safety.
In short, traceability is a key aspect in the development and implementation of machine learning models, as it allows model developers and users to understand and evaluate the training process and model results.
Once the basic concepts for building a commercial software with artificial intelligence are clear, where it is defined to whom to dedicate effort and [...]
Read More »The current scenario we are experiencing in Spain with the COVID-19 health crisis has led to many companies having to carry out ER [...]
Read More »The world is experiencing exponential growth in data generation on an ever-increasing scale. According to IDC (International Data Corp.
Read More »Artificial intelligence (AI), Machine Learning (ML) and data analytics are rapidly changing and having a major impact on our business.
Read More »
