Operation Guide

ModelArts provides online code compiling environments as well as AI development lifecycle that covers data preparation, model training, model management, and service deployment for developers who are familiar with code compilation, debugging, and common AI engines, helping the developers build models efficiently and quickly.

This document describes how to perform AI development on the ModelArts management console. If you use the APIs or SDKs for development, view ModelArts SDK Reference or ModelArts API Reference.

To view the examples of AI development lifecycle, see Getting Started and Best Practices.

AI Development Lifecycle

AI development lifecycle provided by 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.

**Table 1** Process description
Task	Sub Task	Description	Reference
Development	Creating a Notebook Instance	Create a notebook instance as the development environment.	Creating a Notebook Instance
Development	Compiling Debugging Code	Compile code in an existing notebook to directly build a model.	Introduction to JupyterLab and Common Operations
Training a Model	Selecting an Algorithm	Before creating a training job, you need to select an algorithm. You can subscribe to a preset ModelArts algorithm or use your own algorithm.	Introduction to Algorithm Preparation
Training a Model	Creating a Training Job	Create a training job, and use the compiled training script. After training is complete, a model is generated and stored in OBS.	Creating a Training Job
Managing AI Applications	Compile 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.	Introduction to Model Package Specifications
Managing AI Applications	Creating an AI Application	Import a trained model to ModelArts to create an AI application, facilitating AI application deployment and publishing.	Creating an AI Application
Deploying AI Applications	Deploying a Model as a Service	Deploy a model as a real-time service or a batch service.	Deploying as a Real-Time Service Deploying as a Batch Service
Deploying AI Applications	Accessing the Service	If the model is deployed as a real-time service, you can access and use the service. If the model is deployed as a batch service, you can view the prediction result.	Accessing Real-Time Services Viewing the Batch Service Prediction Result