Scalable AutoML for Time Series Forecasting Using Ray

This course aims to teach learners how to apply Scalable AutoML for Time Series Forecasting using Ray. The course covers the process of feature generation and selection, model selection, and hyper-parameter tuning in a distributed fashion. The teaching method involves sharing real-world experiences and takeaways from earlier users. The intended audience for this course includes individuals interested in machine learning applications for time series forecasting and those looking to automate the process using AutoML and Ray.