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Learning means identifying and capturing historical patterns from the data, and inference means mapping a current value to the historical pattern. The following figure illustrates the idea of a large cluster of GPUs being used for learning, followed by a smaller number for inference.
The platform can assign specific roles to team members involved in the packaging process and grant them access to relevant aspects such as datapreparation, training, deployment, and monitoring. Developers can deploy their models on a cluster of servers and use Kubernetes to manage the resources needed for training and inference.
Nobody else offers this same combination of choice of the best ML chips, super-fast networking, virtualization, and hyper-scale clusters. This typically involves a lot of manual work cleaning data, removing duplicates, enriching and transforming it.
Fastweb , one of Italys leading telecommunications operators, recognized the immense potential of AI technologies early on and began investing in this area in 2019. With a vision to build a large language model (LLM) trained on Italian data, Fastweb embarked on a journey to make this powerful AI capability available to third parties.
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