Autonomous agent designed to analyse and solve multivariate classification and regression problems using tabular data and time series. The solution includes continuous and distributed learning capabilities, allowing it to adapt and train over time as it processes dynamic data streams. It can also be grouped with other similar agents to share acquired knowledge and computing workloads.
Its approach combines automated analysis, incremental adaptation and collaboration between agents to address modelling problems in contexts where data evolve and learning needs to be updated progressively.
Classification and regression on dynamic data
LDistributed Continual Learning Agent analyses multivariate classification and regression problems in tabular data and time series. The agent processes dynamic data streams and learns incrementally, adapting its training over time. It can also work alongside other similar agents to share acquired knowledge and computing resources in distributed-learning environments.
Continuous learning and collaboration between agents
Thanks to its distributed-learning capabilities, the agent can collaborate with other similar agents, sharing knowledge and computing workloads. This enables the analysis and training to adapt progressively as the available information evolves.
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