Machine Learning Platform for AI:What is Machine Learning Platform for AI?

Machine learning uses statistical algorithms to train models based on a large amount of historical data, and uses the generated models to help you make informed business decisions. Machine learning can be applied to the following scenarios:

• Marketing: commodity recommendation, user profiling, and targeted advertising.

• Finance: credit risk prediction for loans, financial risk management, stock forecast, and gold price forecast.

• Social network: analytics of key opinion leaders and relational networks.

• Text processing: news classification, keyword extraction, text summarization, and text analytics.

• Unstructured data processing: image classification and text extraction based on optical character recognition (OCR).

• Other forecast scenarios: rainfall forecast and football match result forecast.

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

• Supervised learning: Each sample has an expected value. You can create a model to map input feature vectors to target values. Supervised learning can be used to solve regression and classification issues.

• Unsupervised learning: Samples do not have target values. Unsupervised learning is used to discover potential regular patterns from the sample data. You can use unsupervised learning to solve clustering issues.

• Reinforcement learning: This learning mode is complex. A system constantly interacts with the external environment to obtain feedback and determines its own behavior to achieve a long-term optimization of targets. Examples of reinforcement learning are AlphaGo and autonomous driving.

Machine Learning Platform for AI supports the following underlying computing frameworks:

• Flink, a stream computing framework.

• TensorFlow, an optimized deep learning framework based on open source TensorFlow.

• Parameter Server, a computing framework that can process hundreds of billions of samples in parallel.

• Spark, PySpark, MapReduce, and other mainstream open source computing frameworks.

Machine Learning Platform for AI provides the following services:

• Machine Learning Designer: a service for visualized modeling and distributed training, For more information, see Visualized Modeling in Machine Learning Designer.

• Data Science Workshop (DSW): a Notebook-based service for interactive AI research and development, For more information, see DSW Notebook Service.

• Deep Learning Containers (DLC): a basic cloud-native platform for AI, For more information, see PAI-DLC.

• Elastic Algorithm Service (EAS): a service that allows you to deploy models as online prediction services, For more information, see EAS Model Serving.

Machine Learning Platform for AI provides the following benefits:

• Services provided by Machine Learning Platform for AI can be used separately or in combination. Machine Learning Platform for AI provides an all-in-one platform for machine learning. After training data is prepared in Object Storage Service (OSS) or MaxCompute, you can use Machine Learning Platform for AI to streamline all workflows, including data uploading, data preprocessing, feature engineering, model training, model evaluation, and model publishing (to both online and offline environments).

• Machine Learning Platform for AI can be integrated with DataWorks and allows you to process data by using SQL, user-defined functions (UDFs), user-defined aggregation functions (UDAFs), and MapReduce. This ensures higher flexibility and efficiency.

• Experiments that are used to train and generate models can be scheduled in DataWorks. You can run scheduled tasks in the staging or production environment. This enables data isolation.