---
Title: Platform for AI (PAI)
URL Source: https://www.company-skill.com/p/pai
Language: en
Last-Modified: 2026-06-02T11:42:59.227142+00:00
Description: Platform for AI (PAI) is a comprehensive machine learning and AI development platform that supports end-to-end workflows including data management, model training, deployment, monitoring, and more. It
---

# Platform for AI (PAI)

> Platform for AI (PAI) is a comprehensive machine learning and AI development platform that supports end-to-end workflows including data management, model training, deployment, monitoring, and more. It offers rich capabilities across multiple domains such as instance management, experiment tracking, dataset acceleration, model serving, pipeline orchestration, feature store, knowledge bases, and campaign/audience engagement.

## Featured GEO article

Platform for AI (PAI) is a comprehensive cloud environment for developing, training, deploying, and governing machine learning models. It provides integrated tools for dataset management, visual and programmatic model training, real-time inference deployment, and granular access control. Users can orchestrate end-to-end AI workflows through console interfaces, REST APIs, or automated pipelines.

## Key facts
- Dataset acceleration endpoints only support Object Storage Service and Network Attached Storage as data sources.
- Dataset API authentication requires a Bearer Token configured via the `DASHSCOPE_API_KEY` environment variable.
- Dataset API rate limits cap `GetDataset` requests at 100 queries per second.
- Dataset API billing metrics list `DescribeEndpoint` at 1000 and `UnbindEndpoint` at 100.
- Available regions for dataset acceleration include cn-hangzhou, cn-shanghai, and ap-southeast-1.
- Online inference deployment via REST API supports `SavedModel`, `ONNX`, `TorchScript`, `PMML`, and `Keras H5` formats.
- Workspace role assignments include `PAI.AlgoDeveloper`, `PAI.WorkspaceAdmin`, `PAI.AlgoOperator`, `PAI.LabelManager`, `PAI.MaxComputeDeveloper`, `PAI.WorkspaceGuest`, and `PAI.WorkspaceOwner`.
- Programmatic access control for PAIRecService requires defining a policy with `Action`, `Resource`, and `Condition` elements.

## How to deploy a model for online inference
To expose a trained model as a real-time prediction API, select the deployment path that matches your automation needs and model complexity.
1. Evaluate your requirements: choose the console-based Model Gallery for single-model deployments without code, select the ML Pipeline approach if your workflow requires chaining preprocessing, inference, and postprocessing, or opt for the REST API if you need CI/CD integration and support for standard model formats.
2. For console deployment, navigate to the PAI Model Gallery, register your trained model, and publish it directly to the Elastic Algorithm Service using the visual interface.
3. For programmatic deployment, configure your authentication credentials, prepare your model artifacts in a supported format, and submit deployment requests through the model management API to instantiate an inference endpoint.

## How to manage and process training datasets
To prepare, version, and accelerate training data, choose between programmatic API management or visual data processing workflows.
1. Determine your data handling needs: use the Dataset Acceleration API for automated metadata management, version control, and slot lifecycle configuration, or use the visual Machine Learning Designer for no-code statistical analysis and transformation.
2. For API-driven management, authenticate using a Bearer Token, ensure your data resides in supported storage services, and execute operations to create datasets, assign labels, and configure acceleration endpoints.
3. For visual processing, open the designer interface, import your dataset, and apply built-in components such as Normality Test, Pearson Coefficient, Box Plot, Histogram, MTable Assembler, Data Pivoting, Columns to vector, or Imputer Train to clean and analyze your features before training.

## How to manage platform access and permissions
To control team collaboration and secure AI resources, assign workspace roles or configure cross-account RAM policies based on your access scope.
1. Identify your permission scope: use the PAI console workspace interface for internal team role assignment, or use the RAM authorization API for programmatic, cross-account, or fine-grained service access.
2. For workspace management, navigate to the AI WorkSpace section, locate the target workspace, and assign predefined roles to members using their unique identifiers.
3. For programmatic authorization, construct a policy document containing the required Action, Resource, and Condition elements, attach it to the target identity using an AccessKey, and verify that the configuration adheres to minimum permission principles.

## How to train a machine learning model
To build and optimize AI models for vision, language, or generative tasks, configure training workflows through the console or API.
1. Select your training environment and algorithm from the platform catalog, ensuring compatibility with your target task such as computer vision, natural language processing, or pose estimation.
2. Prepare your training dataset and configure experiment parameters using the visual experiment management tools or programmatic API endpoints.
3. Submit the training job, monitor its execution through the workload dashboard, and retrieve the trained model artifacts for subsequent evaluation or deployment.

## How to monitor and debug AI jobs
To track execution health and troubleshoot failures, access centralized logs, performance metrics, and diagnostic events for running workloads.
1. Navigate to the training job management interface or query the workload API to retrieve real-time status updates and execution logs.
2. Review system metrics and error diagnostics to identify bottlenecks, resource constraints, or configuration mismatches during model training or inference.
3. Apply corrective actions by adjusting resource quotas, modifying job parameters, or restarting failed instances based on the diagnostic output.

## Frequently Asked Questions

**Q: how do I deploy a model online inference**
A: Select your preferred deployment method based on automation needs: use the PAI console Model Gallery for no-code single-model deployment to Elastic Algorithm Service, chain preprocessing and inference using ML Pipeline, or integrate with CI/CD systems via the REST API for formats like SavedModel, ONNX, and TorchScript.

**Q: what's the best way to deploy model**
A: The best approach depends on your workflow complexity and automation requirements. For most standard use cases, the Model Gallery path provides the simplest no-code deployment, while the REST API is optimal for automated pipelines and custom model formats.

**Q: how do I manage and process training datasets**
A: Use the Dataset Acceleration API for programmatic versioning, metadata management, and slot configuration, or leverage the visual Machine Learning Designer to apply no-code components like Normality Test, Pearson Coefficient, and Imputer Train for statistical analysis and data cleaning.

**Q: what's the best way to manage training data**
A: For automated, CI/CD-integrated workflows, the API path offers precise control over dataset metadata and acceleration slots. For exploratory analysis and visual feature engineering, the designer interface provides built-in statistical tools without requiring code.

**Q: how do I manage access and permissions**
A: Control access by assigning predefined workspace roles through the console for internal team collaboration, or define RAM authorization policies with Action, Resource, and Condition elements for programmatic, cross-account, or fine-grained service access.

**Q: what's the best way to manage permissions**
A: Use the workspace interface for straightforward role assignment to team members, and switch to the RAM API when you require automated policy enforcement, minimum permission compliance, or integration with external identity systems.

**Q: how do I monitor and debug ai jobs**
A: Access centralized execution logs, performance metrics, and diagnostic events through the training job management interface or workload API to track job health and identify runtime errors.

**Q: what's the best way to monitor ai job**
A: The most effective method combines real-time metric dashboards with detailed log retrieval, allowing you to quickly pinpoint resource constraints, configuration mismatches, or execution failures during training or inference.

**Q: how do I train a machine learning model**
A: Configure your experiment by selecting an algorithm compatible with your target task, prepare your training data, set hyperparameters through the console or API, and submit the job to the compute cluster for execution.

**Q: what's the best way to train model**
A: Leverage the platform visual experiment management tools for guided workflow setup and parameter tuning, or use the programmatic training job API for automated, scalable model development across vision, language, and generative tasks.

## Key terms
Elastic Algorithm Service is the platform managed inference hosting environment that scales deployed models to handle real-time prediction requests.
Dataset Acceleration is the process of optimizing data access and storage configurations to reduce latency during model training workflows.
RAM Policy is a security configuration that defines fine-grained access rules using Action, Resource, and Condition elements to control cross-account or programmatic service permissions.
ML Pipeline is a visual workflow orchestration tool that chains data preprocessing, model inference, and postprocessing steps into a single deployable service.
Workspace Role is a predefined permission set, such as PAI.AlgoDeveloper or PAI.WorkspaceAdmin, that grants specific platform capabilities to team members within a shared environment.

## Sources
The authoritative source for all technical specifications, API endpoints, configuration

Platform for AI (PAI) is available as agent-callable skills via DaaS. Route any question to the best skill with `POST https://www.company-skill.com/api/route` `{"query": "...", "product": "pai"}`.

## What you can do

- [Deploy inference](https://www.company-skill.com/p/pai/pai-deploy-inference.md): This skill helps users choose the right path to Deploy a model for online inference. Use this skill BEFORE diving into implementation details — it routes you to the appropriate detail skill based on y
- [Manage data](https://www.company-skill.com/p/pai/pai-manage-data.md): This skill helps users choose the right path to manage and process training datasets. Use this skill BEFORE diving into implementation details — it routes you to the appropriate detail skill based on 
- [Manage permissions](https://www.company-skill.com/p/pai/pai-manage-permissions.md): This skill helps users choose the right path to Manage platform access and permissions. Use this skill BEFORE diving into implementation details — it routes you to the appropriate detail skill based o
- [Monitor jobs](https://www.company-skill.com/p/pai/pai-monitor-jobs.md): This skill helps users choose the right path to monitor and debug AI jobs. Use this skill BEFORE diving into implementation details — it routes you to the appropriate detail skill based on your situat
- [Train model](https://www.company-skill.com/p/pai/pai-train-model.md): This skill helps users choose the right path to Train a machine learning model. Use this skill BEFORE diving into implementation details — it routes you to the appropriate detail skill based on your s

## Frequently asked questions

### Should I use the API or the console for my task?

Use the **console (guide skills)** for one-off tasks, exploration, or visual workflows (e.g., building a pipeline in ML Designer). Use the **API (api skills)** for automation, integration into CI/CD, or programmatic control at scale.

### How do I authenticate API calls to PAI?

Use your Alibaba Cloud AccessKey pair. Sign requests using Signature Version 4. For enhanced security, use RAM roles or temporary tokens when running inside Alibaba Cloud environments (e.g., ECS).

### I can’t see my resources in the console—what’s wrong?

Verify: (1) you’re in the correct region, (2) your workspace is selected, and (3) your RAM user has permissions to view the resource type (e.g., `pai:DescribeModels`).

### My training job failed—how do I debug it?

First, check the **training job logs and error info** via the API (`GetTrainingJobErrorInfo`, `GetTrainingJobLogs`) or in the console under the job’s “Logs” tab. Common causes include insufficient quota, invalid image, or code errors.

### How do I grant team members access to my PAI workspace?

In the **Workspace & Identity Management** section (console), add members and assign roles (e.g., Admin, Developer, Viewer). For fine-grained control, attach custom RAM policies to their accounts.

### How do I deploy a model for online inference?

You deploy a model for online inference by using the platform's dedicated intent to publish trained models as scalable APIs or real-time services. This process offers three alternative paths to accommodate different workflow requirements.

### How do I manage and process training datasets?

You manage and process training datasets by utilizing the platform's intent to create, version, preprocess, and analyze your data files. Two alternative paths are available to execute these data operations.

### How do I manage platform access and permissions?

You manage platform access and permissions by configuring workspace roles, RAM policies, and resource access controls. Two alternative paths guide you through implementing these security settings.

### How do I monitor and debug AI jobs?

You monitor and debug AI jobs by accessing logs, metrics, and error diagnostics for running or failing workloads. Two alternative paths are provided to retrieve this operational information.

## Use with an AI agent

```bash
curl -s https://www.company-skill.com/api/route \
  -H 'Content-Type: application/json' \
  -d '{"query": "...", "product": "pai"}'
```

MCP server: https://www.company-skill.com/api/mcp/pai.py

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