Continuous Learning
Continuously improve artificial intelligence models with custom learning lifecycles.
Sampling
Artificial Intelligence models are able to learn to sample a subset of data points from a larger dataset (usuarlly very large ones) to match one or many goals or criterias. The goal of sampling is to ensure that the selected data points are somehow meaningfully connected to the overall dataset, while also reducing the computational burden and time required to process large amounts of data.
Clustering
Artificial Intelligence models are able to learn to sample a subset of data points from a larger dataset (usuarlly very large ones) to match one or many goals or criterias. The goal of sampling is to ensure that the selected data points are somehow meaningfully connected to the overall dataset, while also reducing the computational burden and time required to process large amounts of data.
Anomaly detection
Is a technique used in statistics to identify data points that deviate from the expected behavior of a system. Using AI techologies this is can be used systematically and extensively analyzing patterns and identifying outliers that may indicate unusual ocurrences.
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