This topic aims to help you quickly get familiar with and use Platform for AI (PAI). You can understand the key modules and common use scenarios of PAI and the billing methods of the modules. You can also get familiar with the common use cases of PAI and gain valuable operational experience.
Overview
PAI is a machine learning and deep learning engineering platform for developers and enterprises. PAI offers end-to-end Artificial Intelligence (AI) development services, including data labeling, model development, model training, model deployment, and inference optimization. In addition, PAI provides more than 140 built-in optimization algorithms and a variety of plug-ins for AI-empowered industries to implement easy-to-use and high-performance cloud-native AI engineering capabilities. |
Terms related to basic modules
The terms related to the following basic modules help you understand the core features and use scenarios of PAI.
Common use scenarios
AI paintingScenario description:
Related model: Stable Diffusion Involved modules in PAI: DSW and EAS References: | Large language model (LLM)Scenario description:
Related models: Qwen, Llama, Baichuan series, and other models Involved modules in PAI: DSW and EAS References: | Retrieval-Augmented Generation (RAG)-based LLM chatbotScenario description:
Related models: Qwen, Llama, Baichuan series, and other models Involved module in PAI: EAS References: | ComfyUI-based AI video generationScenario description:
Related model: Stable Video Diffusion Involved module in PAI: EAS References: |
LLM data processingScenario description:
Related algorithms: LLM data processing algorithms Involved module in PAI: Machine Learning Designer References: | Image-text pair filteringScenario description:
Related algorithms: LVM image preprocessing algorithms Involved module in PAI: Machine Learning Designer References: | Intelligent labelingScenario description:
Involved module in PAI: iTAG References: | Large-scale distributed trainingScenario description:
Involved module in PAI: DLC References: |
Overview of PAI modules
QuickStartQuickStart integrates various high-quality pre-trained models in open source AI communities. You can quickly get started with model-related operations, such as fine-tuning, deploying, and evaluating models. | iTAGiTAG allows you to label different types of data, such as images, text, videos, and audios, or multimodal data. iTAG provides a wide range of labeling content and topic components. You can use common labeling templates that are provided by iTAG or create custom labeling templates based on your business scenarios. | Machine Learning DesignerMachine Learning Designer provides various built-in and proven machine learning algorithms to meet your business requirements in different scenarios, such as product recommendation, financial risk management, and advertising prediction. In addition, Machine Learning Designer provides an end-to-end visualized environment for modeling development. | DSWDSW integrates multiple cloud development environments, such as JupyterLab, WebIDE, and Terminal, for coding, debugging, and job running. DSW provides various heterogeneous computing resources and open source framework images and allows you to mount datasets of the Object Storage Service (OSS), File Storage NAS (NAS), and Cloud Parallel File Storage (CPFS) types. You can manage the lifecycles of DSW instances and use DSW for development in an efficient manner. |
DLCDLC provides a flexible, stable, easy-to-use, and high-performance machine learning and training environment. DLC supports multiple algorithm frameworks, including custom algorithm frameworks, and can implement large-scale distributed deep learning jobs. DLC allows you to enjoy an optimal training environment and can improve the training efficiency and reduce costs. | EASEAS allows you to deploy models as online inference services or AI-powered web applications. EAS is suitable for multiple AI inference scenarios, such as real-time inference and near-real-time asynchronous inference. EAS supports automatic scaling and has a complete monitoring and maintenance system. | Scenario-based solutionsThe PAI console provides various scenario-based solutions. |
Billing
Billing method | Description | Involved module |
Pay-as-you-go | If you use the pay-as-you-go billing method, you are charged based on the actual usage of each module. The pay-as-you-go billing method is suitable for short-term or uncertain workloads. It allows you to pay for resources based on the actual amount of resources that you use. The pay-as-you-go billing method is suitable for test environments, development environments, unexpected requirements, or projects in the early phases. | Machine Learning Designer, DSW, DLC, and EAS |
Subscription | The subscription billing method is suitable for long-term and stable workloads. You must pay in advance to use resources for a specific period of time, such as a month or a year. The subscription billing method is more cost-effective than the pay-as-you-go billing method for long-term use. | DSW, DLC, and EAS |
Resource plan | Resource plans refer to quota plans of specific resources that you can purchase in advance. Resource plans are suitable for scenarios in which you want to use a large number of specific resources. You can purchase quota plans for specific resources at more favorable prices. | DSW |
Savings plan | You can purchase savings plans in advance, which offer specific discounts or benefits. Savings plans provide discounted pay-as-you-go rates in exchange for committing to a specific spending amount within a specific period of time. | DSW and EAS |
Pay-by-inference-duration | You are charged based on the actual inference duration. The resource specifications support automatic scaling based on the number of service requests. This billing method is suitable for inference tasks that require indefinite quantities and is appropriate for high-concurrent requests and dynamic loads. | EAS |
Common cases
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