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.)

2023-08-18

13:30 GMT+8 Distributed caching for generative AI: Optimizing the LLM data pipeline on the cloud Chinese Session 傅正佳

14:00 GMT+8 Bytedance Spark supports Wanka model inference practices Chinese Session 刘畅,张永强

14:30 GMT+8 Why do we need a compiler architecture for heterogeneous computing Chinese Session 王臣汉

15:00 GMT+8 Analysis and application of new features of Flink ML 2.2.0 Chinese Session 张智鹏,洪帆

15:45 GMT+8 Bringing LLM to Everywhere via Machine Learning Compilation English Session Siyuan Feng

16:15 GMT+8 Bytedance deep learning batch flow integrated training practice Chinese Session 毛洪玥

16:45 GMT+8 Unifying Real-time and Batch ML Inference using BentoML and Apache Spark Chinese Session Bo Jiang

17:15 GMT+8 Boost ML networks on specific HW platform with Apache TVM on the example of Qualcomm Adreno™ GPU English Session Egor Churaev