modelarts-umn/umn/source/getting_started_with_modelarts.rst

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Getting Started with ModelArts

ModelArts is easy to use for users with different experience.

• For service developers without AI development experience, you can use ExeML of ModelArts to build AI models without coding.
• For developers who are familiar with code compilation, debugging, and common AI engines, ModelArts provides online code compiling environments as well as AI development lifecycle that covers data preparation, model training, model management, and service deployment, helping the developers build models efficiently and quickly.

ExeML

ExeML is a customized code-free model development tool that helps users start AI application development from scratch with high flexibility. ExeML automates model design, parameter tuning and training, and model compression and deployment with the labeled data. Developers do not need to develop basic and encoding capabilities, but only to upload data and complete model training and deployment as prompted by ExeML.

For details about how to use ExeML, see Introduction to ExeML <modelarts_21_0001>.

AI Development Lifecycle

The AI development lifecycle on ModelArts takes developers' habits into consideration and provides a variety of engines and scenarios for developers to choose. The following describes the entire process from data preparation to service development using ModelArts.

Figure 1 Process of using ModelArts

Table 1 Process description

Task	Sub Task	Description	Reference
Data preparation	Creating a dataset	Create a dataset in ModelArts to manage and preprocess your business data.	Creating a Dataset <modelarts_23_0004>
	Labeling data	Label and preprocess the data in your dataset based on the business logic to facilitate subsequent training. Data labeling affects the model training performance.	Labeling Data <modelarts_23_0010>
	Publishing a dataset	After labeling data, publish the database to generate a dataset version that can be used for model training.	Publishing a Dataset <modelarts_23_0018>
Development	Creating a notebook instance	Create a notebook instance as the development environment.	Creating a Notebook Instance <modelarts_23_0034>
	Compiling code	Compile code in an existing notebook to directly build a model.	Opening a Notebook Instance <modelarts_23_0325> Common Operations on Jupyter Notebook <modelarts_23_0120>
	Exporting the .py file	Export the compiled training script as a .py file for subsequent operations, such as model training and management.	Using the Convert to Python File Function <modelarts_23_0037>
Model training	Creating a training job	Create a training job, upload and use the compiled training script. After the training is complete, a model is generated and stored in OBS.	Introduction to Training Jobs <modelarts_23_0046>
	(Optional) Creating a visualization job	Create a visualization job (TensorBoard type) to view the model training process, learn about the model, and adjust and optimize the model. Currently, visualization jobs only support the MXNet and TensorFlow engines.	Managing Visualization Jobs <modelarts_23_0050>
Model management	Compiling inference code and configuration files	Following the model package specifications provided by ModelArts, compile inference code and configuration files for your model, and save the inference code and configuration files to the training output location.	Model Package Specifications <modelarts_23_0091>
	Importing a model	Import the training model to ModelArts to facilitate service deployment.	Introduction to Model Management <modelarts_23_0052>
Model deployment	Deploying a model as a service	Deploy a model as a real-time or batch service.	Deploying a Model as a Real-Time Service <modelarts_23_0060> Deploying a Model as a Batch Service <modelarts_23_0066>
	Accessing the service	After the service is deployed, access the real-time service, or view the prediction result of the batch service.	Accessing a Real-Time Service (Token-based Authentication) <modelarts_23_0063> Viewing the Batch Service Prediction Result <modelarts_23_0067>