The workshop will focus on different deployment designs of machine learning pipelines using R, open-source applications, and free-tier tools. We will use the US hourly demand for electricity data from the EIA API to demonstrate the deployment of a pipeline with GitHub Actions and Docker that fully automates the data refresh process and generates a forecast on a regular basis. This includes the use of open-source tools such as point-blank to monitor the health of the data and the model's success. Last but not least, we will use Quarto doc to set up the monitoring dashboard and deploy it on GitHub Pages.
Rami Krispin
Rami Krispin is a data science and engineering manager who mainly focuses on time series analysis, forecasting, and MLOps applications. He is the author of Hands-On Time Series Analysis with R and is currently working on my next book, Applied Time Series Analysis and Forecasting, which focuses on forecasting at scale. He is passionate about open source, putting stuff into production, working with data and APIs, machine learning, Bayesian statistics, data visualization, and GIS data.
