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How Do AI Beginners Use ModelArts?

Updated at: Aug 12, 2019 GMT+08:00

AI beginners with certain AI knowledge can use your own business data and select common algorithms (ModelArts built-in algorithms) for model training to obtain new models.

For details about how to use a built-in algorithm to build a model, see AI Beginners: Using a Built-in Algorithm to Build a Model.

Table 1 Usage process





Data preparation

Creating a dataset

Use your own business data to create a dataset in ModelArts to manage and preprocess your data.

Creating a Dataset

Labeling data

Label the data in your dataset based on the service logic to facilitate subsequent training. The data labeling affects the model training effect.

Labeling Data

Publishing the dataset

After the data is labeled, publish the dataset to generate a dataset version for model training.

Publishing a Dataset

Model training

Creating a training job

Create a training job and select a built-in algorithm to train a model. After the training is completed, the generated model is stored in OBS.

Introduction to Built-in Algorithms

Creating a Training Job

(Optional) Creating a TensorBoard job

Create a TensorBoard job to view the model training process, learn about the model, and adjust and optimize the model. TensorBoard applies only to the MXNet and TensorFlow engines.

Managing a TensorBoard Job

Model management

Importing a model

Import the trained model to ModelArts to facilitate model deployment.

Importing a Model

Service deployment

Deploying a service

Deploy the model as a real-time or batch service.

Accessing the service

After the service is deployed, access the real-time or edge service, or view the prediction result of the batch service.

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