You can quickly and easily build machine learning experiments using the drag-and-pull method.
The computing results of the entire machine learning process can be visually displayed.
Provides more than 100 algorithm modules for regression, classification, clustering, text analysis, relationship mining, and many other models.
Supports preprocessing tools and software, feature engineering, analysis systems, application areas, common machine learning algorithms, financial algorithms.
Provides a comprehensive service experience by helping users implement data cleansing, feature engineering, machine learning algorithms, evaluation, online prediction, and offline scheduling on the same platform.
Through consistent optimization of asynchronous and parallel communication, Machine Learning Platform for AI supports high-throughput and low-latency parameter exchange, I/O of multiple types of streaming data, and exactly-once failover. This strategy supports the training tasks that involve tens of billions of features and hundreds of billions of parameter models. This strategy is backed by the rich experience accumulated through long-term support of algorithm services. Alibaba has developed an integrated algorithm system that covers the linear model, decision tree, to deep sparse model. In a large-scale sparse feature scenario, Machine Learning Platform for AI supports dynamic feature control that enables addition and removal of model features at any time, providing a powerful tool for CTR prediction scenarios such as recommendations, advertising, and search.