This episode (recorded late 2023) of Inspiring Computing features Nikki, a tech lead in machine learning at a company called Adyen. She discusses her journey into AI and our role at adjunct. Nikki initially studied econometrics, but found it lacking in practical application. She then delved into programming, building apps and websites, and eventually combined her love for mathematics and programming.

She worked at KLM as a software engineer, primarily on C++ plus before transitioning to ING. Was she, when she began working on extensively with Python and data related projects. At Adyen Nikki explains that the company facilitates payment processes for various businesses, ensuring integration with different payment methods for companies like streaming services and eclipse Cromer's platforms. She elaborates on the, behind the scenes process of payments, including the risk checks authentication. Emphasizing agent's role as a payment. Gateway and its banking license, which allows for same day payment processing.

Nikki discusses the complexity of payment optimization due to different messaging protocols and the rules across banks, particularly. With visa and MasterCard edge and maintain standards and smooth transition of these protocols. Leveraging machine learning models, trained on past data to adapt, to changes and ensures seamless payment processing across various banks and regions. They experiment different models per region, per group, and companies to optimize this performance.

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In this podcast episode, we delve into the intricacies of power markets and energy forecasting with Tom Lemmens who has firsthand experience in the field. Starting his career at an energy company, our guest explains the complexities of short-term power markets, focusing on generation forecasting for wind and solar power, as well as price forecasting.

We learn about the crucial role of forecasting prices as a proxy for balancing the grid, and the importance of portfolio optimization in maximizing asset value. After transitioning from a data science consultant back to the energy sector, our guest became one of the early joiners at Dexter Energy, a company providing generation forecasting and trade optimization services.

Dexter Energy specializes in forecasting solar and wind power generation, along with short-term power prices, to help companies make informed trade strategies and optimize their assets. The guest highlights the significance of utilizing Python in their work and explains the process of translating data into expected power output using machine learning models.

Moreover, we explore the challenges and rapid changes in the energy transition, particularly in regions with increasing adoption of renewable energy sources like solar panels. Tom shares insights into the continuous evolution of their models and the technology stack used at Dexter Energy, including Python, Google Cloud, Airflow, and various databases.

Finally, we uncover the data sources for weather data, essential for accurate forecasting, and the iterative process of determining model usefulness through backtesting. This episode provides a comprehensive overview of the dynamic energy market and the vital role of data-driven solutions in optimizing energy trading strategies.

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In this episode of Inspiring Computing we talked to Ritchie Vink was the author of polars. Polars is a Python package written in rust and anyone who is ever used pandas will for sure find this episode interesting, as polars has incredible speedups compared to pandas. But in order to do so, it is interesting to hear from , Ritchie, where he got his inspiration from, how did he go about creating such a powerful package and where is he actually going in the future?

And this episode, he talks about some of the optimizations that he implemented.

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