AutoML (Automated Machine Learning)
AutoML (Automated Machine Learning) is the automation of the end-to-end pipeline of applying machine learning to real-world data, replacing manual trial and error with a formal search over data preprocessing…
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Distributed training
Distributed training is the practice of training a single machine learning model using many compute devices in parallel, splitting the data, the model, or both across GPUs, TPUs, or other accelerators that…
Mistral Forge
Mistral Forge, branded by its developer simply as Forge, is an enterprise platform and engineering service from Mistral AI for building custom large language models and related multimodal models on an…
Parameter Server (PS)
The Parameter Server (PS) is a distributed system architecture for training large machine learning models in which one set of machines, the server nodes, holds the global model parameters as a partitioned…
Partitioning strategy
A partitioning strategy in distributed deep learning is the plan that decides how a model and its training data are split across multiple accelerators (typically GPUs or TPUs) so that the workload can be…
Pipelining
Pipelining is a term used in two distinct senses within machine learning and artificial intelligence.