Machine Learning and AI Data Sets
A main goal behind machine learning is to implement artificial intelligence to automatically have systems learn from experience to gain accuracy without needing to program in that knowledge.
Many large businesses are seeking ways to apply more accurate AI via machine learning engines to solve some of their largest problems or bring new solutions to market, such as driverless cars. From new technologies to advances in sciences, uncovering meaning behind previously unseen data can provide leaps of gains in new intelligence.
1. Machine learning requires immense amounts of data as the principle need is for AI to learn from each experience. The more data that can be processed, the more accurate AI results become. Each learning experience can mean many new transactions that must be correlated to many other transactions for full understanding. It can literally be billions of data sets.
2. For larger applications, such as medical diagnosis that might be based on millions of patients, traversing the data for meaningful results will require analyzing billions of potential data sets.
3. High performance is critical for some applications, which is why a graph database is one foundational need for optimal machine learning and AI.
Why Nebula Graph