MLOps and Dockerisation in Forecasting with Rami Krispin

In this episode, we sit down with Rami Krispin, a data scientist at Apple and active producer in forecasting, to explore his journey into forecasting and data science. He shares what first sparked his interest in the field and how that passion led him to develop key contributions, including the Hands-On Time Series Analysis with R book and the TSstudio package. We discuss his motivation for writing the book, who it’s for, and how TSstudio and other R packages he has developed have helped practitioners in the forecasting space. He also gives us a sneak peek into his upcoming book, Applied Time Series Analysis and Forecasting with R, and the new topics it will cover.

We then dive into the challenges of deploying forecasting models at scale and the role of MLOps in making machine learning projects production-ready. As a Docker Captain, our guest explains how Docker has changed his approach to time series forecasting and MLOps. We also discuss best practices for forecasting, common mistakes practitioners make, and strategies for improving reproducibility. Looking ahead, we talk about where time series forecasting is heading, the differences between R, Julia, and Python in this space, and how each ecosystem serves different needs. 

You can follow his work on LinkedIn, subscribe to his newsletter, and stay updated on his latest projects.

Website: https://linktr.ee/ramikrispin
LinkedIn Page: https://www.linkedin.com/in/rami-krispin/