# 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.

Parent topic: Basic Knowledge