Forecasting Impact
Forecasting Impact is a bimonthly podcast that aims to disseminate the science and practice of forecasting by introducing prominent academics, practitioners, and visionaries in the forecasting domain. Our vision is to help grow the forecasting community, foster collaboration between academia, industry, and governments, and promote scientific forecasting and good practices.
We will discuss a range of forecasting topics in economics, supply chain, energy, social goods, AI, machine learning, data analytics, education, healthcare, and more.
Forecasting Impact episodes are also available on the IIF YouTube Channel @IIForecasters.
Podcast Team
Chair and Co-host: Dr. Laila Ahadi-Akhlaghi, Senior Technical Advisor at JSI.
Additional co-hosts:
- Dr. Mahdi Abolghasemi, Lecturer in Data Science at The University of Queensland,
- George Boretos, Founder & CEO at FutureUP,
- Dr. Faranak Golestaneh, Data Science Senior Manager at Commonwealth Bank of Australia,
- Mariana Menchero, Senior Forecaster at Nixtla, and
- Arian Sultan Khan, Data Analyst at VAN
Co-hosts in the past have included: Michał Chojnowski, Shari De Baets, Elaine Deschamps, Dr. Sevvandi Kandanaarachchi, Bahman Rostami-Tabar, Anna Sroginis, and Sarah Van der Auweraer.
We welcome your feedback, questions, and suggestions. Please contact us at forecastingimpact@forecasters.org
Forecasting Impact
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/