**Machine Learning PAI (Platform of Artificial Intelligence)** is Alibaba Cloud's artificial intelligence platform, offering an end-to-end machine learning solution. This article introduces what Machine Learning PAI is.

Machine learning refers to the process by which machines use statistical algorithms to learn from large volumes of historical data and then apply the resulting experience-based models to guide business decisions. Currently, machine learning is primarily applied in the following scenarios:

- **Marketing scenarios**: product recommendations, user profiling, and precision-targeted advertising.
- **Financial scenarios**: loan approval prediction, financial risk control, stock price forecasting, and gold price prediction.
- **Social network relationship mining**: analysis of influential Weibo followers or social relationship graph analysis.
- **Text-related scenarios**: news classification, keyword extraction, article summarization, and textual content analysis.
- **Unstructured data processing scenarios**: image classification or text extraction from images.
- **Other predictive scenarios**: rainfall forecasting or predicting football match outcomes.

Machine learning encompasses both traditional machine learning and deep learning. Traditional machine learning can be categorized as follows:

- **Supervised Learning**: Each training sample comes with a corresponding target value. A model is built to map input feature vectors to these target values—for example, solving regression or classification problems.
- **Unsupervised Learning**: Samples have no target labels; the goal is to uncover hidden patterns or structures within the data itself—for instance, clustering problems.
- **Reinforcement Learning**: A more complex paradigm where an agent continuously interacts with its environment, adjusting its behavior based on feedback to optimize long-term rewards—examples include AlphaGo and autonomous driving systems.

Originally developed to serve internal teams within Alibaba Group (such as Taobao, Alipay, and AutoNavi), the PAI platform was designed to help internal developers use artificial intelligence (AI) technologies more efficiently, simply, and consistently. As PAI evolved, it officially launched as a commercial product in 2018. Today, it serves tens of thousands of enterprise customers and individual developers and stands as one of China’s leading cloud-based machine learning platforms.

At its core, PAI supports multiple computing frameworks:
- Stream processing framework: **Flink**
- Deep learning frameworks deeply optimized from open-source versions: **TensorFlow**, **PyTorch**, **Megatron**, and **DeepSpeed**
- Large-scale parallel computing framework for handling trillions of features and samples: **Parameter Server**
- Industry-standard open-source frameworks: **Spark**, **PySpark**, and **MapReduce**

PAI offers the following services:
- **Designer**: Visual modeling and distributed training. For details, see *Visual Modeling (Designer)*.
- **DSW (Data Science Workshop)**: Interactive AI development via Notebook. For details, see *Interactive Modeling (DSW)*.
- **DLC (Deep Learning Containers)**: Distributed training. For details, see *Distributed Training (DLC)*.
- **EAS (Elastic Algorithm Service)**: Online inference service. For details, see *Model Online Serving (EAS)*.

Built upon years of technical expertise and practical experience from Alibaba Cloud and Alibaba Group, PAI delivers multiple key advantages:

- **End-to-end AI development lifecycle support**: PAI provides a comprehensive, one-stop AI platform covering data labeling, model development, training, optimization, deployment, and AI operations management.
- **Over 140 optimized built-in algorithm components**: Supports industry-standard deep learning frameworks including TensorFlow and PyTorch. Offers core capabilities such as multi-mode operation, deep integration with big data engines, multi-framework compatibility, and custom container images. Delivers cloud-native AI development, training, and deployment solutions.
- **Flexible product deployment options**: Available on public cloud in fully managed or semi-managed modes. Supports both high-performance AI computing clusters and lightweight deployment formats.
- **Industry-leading AI optimization**:
  - High-performance training frameworks optimized for sparse training scenarios, supporting billions to hundreds of billions of sparse features and hundreds of billions to trillions of training samples, with distributed incremental training across thousands of workers.
  - Acceleration for mainstream models (e.g., ResNet50, Transformer+LM) using **PAI Blade**, achieving significant speedups.
  - Services can be used individually or in combination.
- **One-stop machine learning workflow**: Users only need to prepare training data (stored in OSS or MaxCompute). PAI handles all modeling tasks—including data upload, preprocessing, feature engineering, model training, evaluation, and deployment to offline or online environments.
- **Seamless integration with DataWorks**: Supports diverse data processing methods such as SQL, UDFs, UDAFs, and MapReduce, ensuring high flexibility.
- **Scheduled experiment workflows**: Model training pipelines can be periodically scheduled via DataWorks, with clear separation between production and development environments to ensure data security and isolation.