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Using ExeML for Text Classification Preparing Text Classification Data Creating a Text Classification Project Labeling Text Classification Data Training a Text Classification Model Deploying a Text Classification Service Parent topic: Using ExeML for Zero-Code AI Development
Workflow Development Configuring Workflow Parameters Configuring the Input and Output Paths of a Workflow Creating Workflow Phases Creating a Multi-Branch Workflow Creating a Workflow Publishing a Workflow Advanced Workflow Capabilities Parent topic: Using Workflows for Low-Code AI
Installing ma-cli Locally Autocompletion for ma-cli Commands ma-cli Authentication ma-cli image Commands for Building Images ma-cli ma-job Commands for Training Jobs ma-cli dli-job Commands for Submitting DLI Spark Jobs Using ma-cli to Copy OBS Data Parent topic: Using Notebook for AI
Parent topic: Deploying an AI Application as a Service
For details, see Creating an AI Application You can only create models for training jobs in the Running status.
Viewing the Word Frequency The word frequency display function is used to collect statistics on hot words generated during calls after AI inspection is complete. Procedure Sign in to the AICC as a tenant administrator and choose Speech Text Analysis > Word Frequency Display.
Creating a Model Registration Phase Description This phase integrates capabilities of ModelArts AI application management. This enables trained models to be registered in AI Application Management for service deployment and update.
Managing Visualization Jobs You can create visualization jobs of TensorBoard and MindInsight types on ModelArts. TensorBoard and MindInsight can effectively display the change trend of a training job and the data used in the training. TensorBoard TensorBoard effectively displays the
Modifying a Dataset For a created dataset, you can modify its basic information to match service changes. Prerequisites You have created a dataset. Modifying the Basic Information About a Dataset Log in to the ModelArts management console. In the left navigation pane, choose Data
Exporting Data A dataset includes labeled and unlabeled data. You can select images or filter data based on the filter criteria and export to a new dataset or the specified OBS directory. In addition, you can view the task history to learn about the export records. Only datasets of
Publishing a Dataset ModelArts distinguishes data of the same source according to versions labeled at different time, which facilitates the selection of dataset versions during subsequent model building and development. After labeling the data, you can publish the dataset to generate
Viewing Job Details After a training job finishes, you can manage the training job versions and check whether the training result of the job is satisfactory by viewing the job details and Viewing the Evaluation Result. Training Job Details In the left navigation pane of the ModelArts
Data Features Images or target bounding boxes are analyzed based on image features, such as blurs and brightness to draw visualized curves to help process datasets. You can also select multiple versions of a dataset to view their curves for comparison and analysis. Background Data
Auto Grouping To improve the precision of auto labeling algorithms, you can evenly label multiple classes. ModelArts provides built-in grouping algorithms. You can enable auto grouping to improve data labeling efficiency. Auto grouping can be understood as data labeling preprocessing
Deleting a Dataset If a dataset is no longer in use, you can delete it to release resources. After a dataset is deleted, if you need to delete the data in the dataset input and output paths in OBS to release resources, delete the data and the OBS folders on the OBS Console. Procedure
Introduction to Model Training ModelArts provides model training of both the new and old versions. Training management of the old version is only available for its existing users. This tutorial applies only to training management of the old version, which will be discontinued soon
Managing Job Parameters You can store the parameter settings in ModelArts during job creation so that you can use the stored settings to create follow-up training jobs, which makes job creation more efficient. During the operations of creating, editing, and viewing training jobs,
Adding the Evaluation Code After a training job is executed, ModelArts automatically evaluates your model and provides optimization diagnosis and suggestions. For details, see Viewing the Evaluation Result. When you use a built-in algorithm to create a training job, you can view the
Stopping or Deleting a Job Stopping a Training Job In the training job list, click Stop in the Operation column for a training job in the Running state to stop a running training job. After the training job is stopped, its billing stops on ModelArts. If you have selected Save Training
Figure 5 Notebook Job Definitions tab Figure 6 Configuring a scheduled job Parent topic: Using a Notebook Instance for AI Development Through JupyterLab
