SiliconANGLE Model fine-tuning through reinforcement learning is fast becoming one of the most demanding jobs in AI infrastructure. It mixes training, inference and sandboxed agent work in a single loop, often with research code tangled into the systems beneath it. Google LLC has been tackling that problem from inside its Kubernetes engineering group, and recently donated […]
The post Google’s GKE Labs turns reinforcement learning into a Kubernetes workload appeared first on SiliconANGLE.
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Key Terms Explained #
Fine-Tuning The process of taking a pre-trained model and continuing to train it on a smaller, specific dataset to adapt it for a particular task or domain.
Inference Running a trained model to make predictions on new data.
Reinforcement Learning A learning approach where an agent learns by interacting with an environment and receiving rewards or penalties.
Training The process of teaching an AI model by exposing it to data and adjusting its parameters to minimize errors.