PAI Designer overview - Platform For AI - Alibaba Cloud Documentation Center

Service architecture

Create and manage workflows: Build workflows from templates or from scratch, then deploy trained models. Create a workflow.
Rich component and data source library: Hundreds of AI development components and supports data sources, such as MaxCompute and Object Storage Service (OSS), built on Alibaba's best practices.
Visualize and analyze training: Use the visualization dashboard to analyze data, models, and evaluation metrics during training to find the optimal model.
Deploy and manage models: Register workflow models, then deploy as online services or package as composite models. Online prediction.
Collaborate and share: Collaborate on workflows within a workspace. Deploy successful workflows to DataWorks for periodic scheduling or publish as custom templates.
Accounts and permissions: Sign in with an Alibaba Cloud account or RAM user. RAM users need permissions from the Alibaba Cloud account. Cloud product dependencies and permissions: Designer.

Designer provides hundreds of components for various use cases. For more information about the components, see Designer component reference.

Components fall into three categories:

Traditional machine learning components: Algorithm Components for data pre-processing, feature engineering, statistical analysis, anomaly detection, recommendation algorithms, time series, and network analysis.
Deep learning framework components: Vision algorithms and Natural Language Processing algorithms based on the PAI-Easy series, as well as Deep Learning frameworks like TensorFlow and PyTorch.
Custom algorithm components: Components such as SQL Script, Python Script, Notebook Script, and PyAlink Script for custom use cases.

Create a workflow

A visual canvas of component nodes and data flows — the starting point for model development.
Build and debug a model

Drag and drop algorithm components to build models. Select compute resources such as MaxCompute, PAI-DLC, or Flink, to run and complete model debugging and training.
Visualize and analyze

Use the visualization dashboard to review analysis reports and evaluate model performance.
Deploy and predict models
- Online prediction: After training, deploy models as online services with PAI-EAS to make predictions on new data.
- Offline batch prediction: For scheduled, offline batch predictions, submit Designer workflows to DataWorks for periodic scheduling.

Designer billing is based on the resources that components consume at runtime:

Billable item

Billing entity

Billing method

Stop billing

Billing rules

Compute Unit-hour (CU-hour) usage

Component runtime

Pay-as-you-go

Stop the component

Resources consumed by a component are converted into Compute Unit (CU) hours.

Billing formula: Billable amount = Number of CU-hours × Unit price

Where, Number of CU-hours = max(Number of CPU cores × Duration (h), Memory (GB) × Duration (h) / 4)

Pricing and billing details are covered in Billing for Designer.

Start building with the Quick start for Designer.

Artificial Intelligence Recommendation: Implement recommendation recall with FM-Embedding | FM recommendation based on the Alink framework.
Intelligent risk control: Financial risk control using graph algorithms | User churn risk warning.
General use cases: News Classification with Text Analysis | Smog Prediction | Power Plant Output Prediction.

For workflow errors, component issues, or data read failures, check the Designer FAQ.

PAIFlow is Designer's workflow scheduling engine. Submit tasks from Designer to PAIFlow.
The PAIFlow task management page lists all Pipeline tasks from Designer manual runs and DataWorks scheduled runs. Manage workflow tasks.