Help Center/ ModelArts/ User Guide for Senior AI Engineers (To Be Offline)/ Operation Guide
Updated on 2023-07-26 GMT+08:00
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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

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

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

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.

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.