Model Optimizer is a cross-platform command-line tool that facilitates the transition between the training and deployment environment, performs static model analysis, and adjusts deep learning models for optimal execution on end-point target devices.
Model Optimizer process assumes you have a network model trained using supported deep learning frameworks: Caffe*, TensorFlow*, Kaldi*, MXNet* or converted to the ONNX* format. Model Optimizer produces an Intermediate Representation (IR) of the network, which can be inferred with the [Inference Engine](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md).
> **NOTE**: Model Optimizer does not infer models. Model Optimizer is an offline tool that runs before the inference takes place.
The scheme below illustrates the typical workflow for deploying a trained deep learning model:
Below is a simple command running Model Optimizer to generate an IR for the input model:
```sh
python3 mo.py --input_model INPUT_MODEL
```
To learn about all Model Optimizer parameters and conversion technics, see the [Converting a Model to IR](prepare_model/convert_model/Converting_Model.md) page.
> **TIP**: You can quick start with the Model Optimizer inside the OpenVINO™ [Deep Learning Workbench](@ref
