AI / machine learning

Track Chairs : Jerry Tan

Machine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. It has a wide range of application scenarios in industry and plays an increasingly important role.

Three types of topics suitable for this sub forum are as follows:

  • AI Framework and AI library projects focusing on machine learning, including Mxnet, TVM, Mahout, singa, SystemML, etc
  • Based on the traditional Apache big data storage and computing project, specific optimization is made in the machine learning scenario, such as spark mlib, Flink ML,etc
  • Implement machine learning platform in industrial scenario based on Apache project (such as airflow + Kafka + Cassandra + spark, etc.)


14:00 GMT+8 Apache Submarine Cloud native machine learning platform Chinese Session 刘勋

14:40 GMT+8 OpenMLDB: An Enterprise-Grade Feature Platform Built Upon Spark Chinese Session LU MIAN

15:20 GMT+8 Pegasus and Flink's practice in Xiaomi machine learning platform Chinese Session 黄飞

16:00 GMT+8 Catch the P99 by the Tail -- Performance tuning for machine learning reasoning Chinese Session 兰青


14:00 GMT+8 Real-time deep learning training PAI-ODL Chinese Session 刘童璇

14:40 GMT+8 Spark + ONNX + CANN: How to improve the performance and experience of distributed reasoning? Chinese Session 王玺源,姜逸坤,黄之鹏

15:20 GMT+8 Flink ML: Real-time machine learning based on Apache Flink Chinese Session 高赟,张智鹏

16:00 GMT+8 BladeDISC: A deep learning compiler practice that supports dynamic shapes Chinese Session 邱侠斐