AI Platform PAI: Comprehensive AI Development and Machine Learning Solution

AI Platform PAI (Platform for AI), formerly known as Machine Learning Platform PAI, is a machine learning/deep learning engineering platform designed for developers and enterprises. It offers a comprehensive suite of AI development services, encompassing data labeling, model construction, model training, model deployment, and inference optimization. With over 140 optimized algorithms and a wealth of industry-specific plugins, PAI empowers users with accessible, high-performance, cloud-native AI engineering capabilities. It supports various AI applications, including AI painting, large language model applications, and AI video generation.

PAI provides several key features, including: * **Smart Labeling (iTAG):** Supports various data types such as images, text, video, and audio, as well as multimodal hybrid labeling. * **Model Online Service (EAS):** Allows users to deploy models as online inference services or AI-Web applications with one click. * **Visual Modeling (Designer):** Offers a full-link visual modeling development environment with rich and mature machine learning algorithms. * **Interactive Modeling (DSW):** Integrates multiple cloud development environments such as JupyterLab, WebIDE, and Terminal, supporting code writing, debugging, and running. * **Distributed Training (DLC):** Provides a flexible, stable, easy-to-use, and high-performance machine learning training environment.

PAI supports a wide range of use cases, including: * **AI Painting:** Generating high-quality digital artwork for illustrations, concept art, and more. * **Large Language Model Applications:** Automating content generation, data analysis, and customer service. * **RAG-based Large Model Dialogue System:** Enhancing customer service and providing intelligent assistants. * **AI Video Generation based on ComfyUI:** Automatically generating creative marketing videos and educational content. * **Large Language Model Data Processing:** Ensuring data uniqueness, consistency, and privacy through various processing techniques. * **Image-Text Pair Filtering:** Ensuring compliance, optimizing image quality, and generating automatic descriptions. * **Smart Labeling:** Automating the labeling of text, images, audio, and video data for various applications. * **Large-scale Distributed Training:** Accelerating model training for image recognition, NLP, and recommendation systems.

PAI offers a variety of function modules to support different stages of AI development: * **PAI-Quick Start:** Provides pre-trained models for quick start, fine-tuning, training, deployment, and evaluation. * **PAI-Smart Labeling (iTAG):** Supports multiple data types and provides rich labeling content and topic components. * **PAI-Visual Modeling (Designer):** Offers a visual modeling environment with built-in machine learning algorithms. * **PAI-Interactive Modeling (DSW):** Integrates cloud development environments and supports code writing, debugging, and running. * **PAI-Distributed Training (DLC):** Provides a flexible and high-performance machine learning training environment. * **PAI-Model Online Service (EAS):** Supports one-click deployment of models as online inference services or AI-Web applications.

To get started with PAI, you can use the PAI-Quick Start feature, which provides pre-trained models for various AI tasks. You can also explore the different function modules and use cases to understand how PAI can be applied to your specific needs. The platform offers various tutorials and documentation to guide you through the process.

PAI offers several billing methods to suit different needs: * **Pay-as-you-go:** Pay for actual usage, suitable for short-term or uncertain workloads. * **Subscription:** Prepay for a fixed period, suitable for long-term and stable workloads. * **Resource Pack:** Purchase a quota package for specific resources, suitable for scenarios requiring large-scale use of specific resources. * **Savings Plan:** Purchase a discount plan by committing to a certain amount of consumption within a certain period. * **Pay-per-inference duration:** Pay based on the actual inference duration, suitable for scenarios requiring variable inference tasks.

PAI provides numerous practical examples, including: * Deploying and fine-tuning Qwen1.5 series models. * Deploying and fine-tuning Tongyi Qianwen-72B-Chat models. * Deploying and fine-tuning Llama-3 series models. * Fine-tuning, evaluating, and deploying Qwen2.5 large language models. * Deploying and fine-tuning Mixtral-8x7B MoE models. * Deploying and fine-tuning Stable Diffusion V1.5 models to achieve text-to-image generation. * AIGC Stable Diffusion text-to-image Lora model fine-tuning to achieve virtual clothing try-on. * Llama3-8B large model fine-tuning training. * Using LLaMA Factory to fine-tune LlaMA 3 models. * Tongyi Qianwen Qwen fully managed Lingjun best practices. * Responsible AI-Fairness Analysis. * Responsible AI-Error Analysis. * AI painting-SDWebUI deployment. * AI video generation-ComfyUI deployment. * Large model RAG dialogue system. * 5 minutes to use EAS to deploy LLM large language model applications with one click. * 5 minutes to use EAS to deploy Stable Diffusion with one click to realize text-to-image capabilities. * 5 minutes to operate EAS to deploy Tongyi Qianwen model with one click. * LLM large language model data processing-Wikipedia (web text data). * LLM large language model data processing-arXiv (paper data). * LLM large language model data processing-Alpaca-Cot (sft data). * Video data filtering and labeling. * News classification based on text analysis algorithms. * Agricultural loan issuance prediction based on regression algorithms.

PAI offers several hands-on experiments to help you gain practical experience: * Deploy ChatGLM and LangChain applications with one click using PAI-EAS. * Quickly deploy AIGC Stable Diffusion WebUI for AI painting using PAI-EAS. * Fine-tune AIGC Stable Diffusion Lora models in PAI-DSW to achieve virtual clothing try-on. * Deploy AIGC services based on PAI-EAS mounting OSS. * Realize European Cup fan exclusive sticker production with one click in PAI ArtLab. * Introduction to the recommendation system: Use collaborative filtering to achieve product recommendation. * Introduction to the recommendation system: Use the ALS algorithm to predict scores. * PAI-DSW quickly starts AI painting Stable Diffusion WebUI.