Scalable AutoML for Time Series Forecasting Using Ray

Explore scalable AutoML techniques for time series forecasting using Ray in this 22-minute conference talk from Databricks. Dive into the application of Automated Machine Learning to time series forecasting, built on top of the distributed framework Ray. Learn how this toolkit automates feature generation, selection, model selection, and hyper-parameter tuning in a distributed manner. Discover real-world applications in network quality analysis, log analysis for data center operations, and predictive maintenance. Gain insights into classical time series forecasting methods and newer machine learning approaches, including neural networks for sequence modeling. Examine the process of building the AutoML toolkit and hear about real-world experiences from early users like Tencent. Cover topics such as AI on Big Data, time series fundamentals, software stack implementation, runtime training, and the Project Zouwu initiative. Understand the application of these techniques in forecasting network traffic KPIs in the telecommunications industry.