📄 中文摘要
在 Databricks 平台上，针对机器学习推理的扩展问题，研究了多种技术以优化集群的性能。通过案例研究，分析了液态和分区两种推理方式的优缺点，以及加盐和不加盐的策略对性能的影响。研究表明，选择合适的推理方式和数据处理策略能够显著提高集群的效率和响应速度。具体的实施细节和性能测试结果为数据科学家和工程师提供了实用的参考，帮助他们在实际应用中做出更有效的决策。
在 Databricks 上扩展 ML 推理：液态还是分区？加盐还是不加盐？
📄 English Summary
Scaling ML Inference on Databricks: Liquid or Partitioned? Salted or Not?
This study investigates various techniques to optimize machine learning inference on the Databricks platform. It analyzes the advantages and disadvantages of liquid versus partitioned inference methods, as well as the impact of salted versus unsalted strategies on performance. The findings indicate that selecting the appropriate inference method and data processing strategy can significantly enhance cluster efficiency and response times. Detailed implementation guidelines and performance testing results provide practical insights for data scientists and engineers, aiding them in making more effective decisions in real-world applications.