11-06, 10:50–12:20 (US/Eastern), Central Park East
This workshop focuses on the foundation of MLOps, demonstrating the deployment of data and an ML pipeline with a real-life example.
The workshop will focus on different deployment designs of machine learning pipelines using open-source applications and free-tier tools. Demonstrating with the US hourly demand for electricity live data from the EIA API, we will learn how to deploy data and ML pipeline with Python, GitHub Actions, and Docker. This includes the use of open-source tools such as Nixtla's mlforecast library to create a forecast, MLflow and pytest 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.
Previous knowledge expected
Rami Krispin is a senior data science and engineering manager, Docker Captain, and LinkedIn Learning instructor. He mainly focuses on time series analysis, forecasting, and MLOps applications.
He is passionate about open source, working with data, machine learning, and putting stuff into production. He creates content about MLOps and recently released a course - Data Pipeline Automation with GitHub Actions Using R and Python, on LinkedIn Learning, and multiple tutorials about Docker for data science.
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 with Python.