Source: thenewstack.io
Dealing with Distributed Data When Training AI Models
Federated learning is a tool that lets your phone autocorrect your spelling without uploading the entirety of your text. It can also be applied to training artificial intelligence at the data’s source, without pooling it into one location.
The New Stack spoke with Irvine to learn how integrate.ai deploys federated learning to https://thenewstack.io/transparent-ai-explainable-and-trainable-artificial-intelligence/ without pooling data. Integrate.ai offers a software developer kit that creates a preset https://thenewstack.io/how-to-share-data-between-docker-containers/ with libraries and other tools needed to train the model locally.
https://thenewstack.io/nvidia-shaves-up-to-30-off-large-language-model-training-times/, an open source framework to do federated learning, and https://thenewstack.io/federated-learning-lets-data-stay-distributed/ allows the building of federated models, he said.
The New Stack spoke with Irvine to learn how integrate.ai deploys federated learning to https://thenewstack.io/transparent-ai-explainable-and-trainable-artificial-intelligence/ without pooling data. Integrate.ai offers a software developer kit that creates a preset https://thenewstack.io/how-to-share-data-between-docker-containers/ with libraries and other tools needed to train the model locally.
https://thenewstack.io/nvidia-shaves-up-to-30-off-large-language-model-training-times/, an open source framework to do federated learning, and https://thenewstack.io/federated-learning-lets-data-stay-distributed/ allows the building of federated models, he said.
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