Artem Goncharov
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Common Concepts of ModelArts
ExeML
ExeML is the process of automating model design, parameter tuning, and model training, model compression, and model deployment with the labeled data. The process is code-free and does not require developers to have experience in model development. A model can be built in three steps: labeling data, training a model, and deploying the model.
Inference
Inference is the process of deriving a new judgment from a known judgment according to a certain strategy. In AI, machines simulate human intelligence, and complete inference based on neural networks.
Real-Time Inference
Real-time inference specifies a web service that provides an inference result for each inference request.
Batch Inference
Batch inference specifies a batch job that processes batch data for inference.
Resource Pool
ModelArts provides large-scale computing clusters for model development, training, and deployment. There are two types of resource pools: public resource pool and dedicated resource pool. The public resource pool is provided by default. Dedicated resource pools are created separately and used exclusively.