In this episode of Forecasting Impact, hosts Mahdi Abolghasemi and Mariana Menchero speak with Marco Peixeiro, applied data scientist at Nixtla, about the growing importance of explainability in time series forecasting. Marco shares how his work bridges research and practice, from developing deep learning models in NeuralForecast to writing educational resources that make complex forecasting concepts accessible to all. We discuss how explainability builds trust in complex models, the role of ...
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In this episode of Forecasting Impact, hosts Mahdi Abolghasemi and Mariana Menchero speak with Marco Peixeiro, applied data scientist at Nixtla, about the growing importance of explainability in time series forecasting. Marco shares how his work bridges research and practice, from developing deep learning models in NeuralForecast to writing educational resources that make complex forecasting concepts accessible to all. We discuss how explainability builds trust in complex models, the role of ...
MLOps and Dockerisation in Forecasting with Rami Krispin
Forecasting Impact
52 minutes
8 months ago
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 prac...
Forecasting Impact
In this episode of Forecasting Impact, hosts Mahdi Abolghasemi and Mariana Menchero speak with Marco Peixeiro, applied data scientist at Nixtla, about the growing importance of explainability in time series forecasting. Marco shares how his work bridges research and practice, from developing deep learning models in NeuralForecast to writing educational resources that make complex forecasting concepts accessible to all. We discuss how explainability builds trust in complex models, the role of ...
