Alibaba Cloud Platform for AI (PAI) is a one-stop machine learning platform that provides data labeling, model development, model training, and model deployment services. This topic describes what PAI is. 

Machine learning refers to machines using statistical algorithms to learn from large amounts of historical data and then applying the generated experience models to guide business operations. Currently, machine learning is mainly applied in the following scenarios:
Marketing: Product recommendations, user profiling, or targeted advertising.
Finance: Loan issuance predictions, financial risk control, stock market forecasting, or gold price predictions.
Social network relationship mining: Analysis of key opinion leaders on social media platforms like Weibo or social relationship chain analysis.
Text processing: News classification, keyword extraction, article summarization, or text content analysis.
Unstructured data processing: Image classification or extracting text from images.
Other prediction scenarios: Rainfall predictions or football match outcome predictions.

Machine learning includes traditional machine learning and deep learning. Traditional machine learning is divided into the following categories:

Supervised Learning: Each sample has a corresponding target value. Models are built to map input feature vectors to target values. For example, solving regression and classification problems.
Unsupervised Learning: Samples do not have target values, aiming to discover potential patterns within the data itself. For example, solving clustering problems.
Reinforcement Learning: A relatively complex system that continuously interacts with the external environment, deciding its actions based on feedback to achieve optimal goals. Examples include AlphaGo and autonomous driving.

PAI was initially developed as an internal machine learning platform for Alibaba Group (e.g., Taobao, Alipay, and AutoNavi), aiming to make AI technology more efficient, concise, and standardized for internal developers. As PAI evolved, it officially became commercialized in 2018. To date, it has accumulated tens of thousands of enterprise clients and individual developers, making it one of China's leading cloud-based machine learning platforms.

PAI supports multiple computing frameworks at its core, including:

Stream computing framework Flink.
Deep learning frameworks optimized from open-source versions, such as TensorFlow, PyTorch, Megatron, and DeepSpeed.
Large-scale parallel computing frameworks like Parameter Server, capable of handling billions of features and samples.
Mainstream open-source frameworks like Spark, PySpark, and MapReduce.

Services provided by PAI include:

Designer: Visual modeling and distributed training. For more details, refer to Visual Modeling (Designer).
DSW (Data Science Workshop): Interactive AI development via Notebook. For more details, refer to Interactive Modeling (DSW).
DLC (Deep Learning Containers): Distributed training. For more details, refer to Distributed Training (DLC).
EAS (Elastic Algorithm Service): Online prediction. For more details, refer to Model Online Service (EAS).

Leveraging years of application and technical expertise from Alibaba Cloud and Alibaba Group, PAI offers the following advantages:

Full lifecycle AI R&D: Supports the entire process from data labeling, model development, model training, optimization, deployment, to AI operation and maintenance management, making it a one-stop AI platform.
Over 140 optimized built-in algorithm components. Supports various deep learning frameworks such as TensorFlow and PyTorch. Provides core capabilities like multi-mode integration, big data engine compatibility, multi-framework support, and custom image functionality. Offers cloud-native architecture for AI development, training, and deployment.
Diverse product output methods: Public cloud supports fully managed and semi-managed solutions. Supports high-performance AI computing clusters and lightweight product forms.
Industry-leading AI optimizations: High-performance training frameworks for sparse training scenarios, supporting tens of billions to hundreds of billions of sparse features, hundreds of billions to trillions of samples, and distributed incremental training with thousands of workers. Accelerates mainstream models like ResNet50 and Transformer+LM through PAI Blade, enhancing performance ratios. These services can be used independently or in combination.
One-stop machine learning: You only need to prepare your training data (stored in OSS or MaxCompute). All modeling tasks, including data upload, preprocessing, feature engineering, model training, evaluation, and deployment (to offline or online environments), can be achieved through PAI.
Integration with DataWorks supports various data processing methods like SQL, UDF, UDAF, and MR, offering high flexibility. The experimental workflow for model training supports periodic scheduling via DataWorks, distinguishing between production and development environments to ensure data security isolation.