Inspiring Computing

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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In this episode, we talk to Thomas who works at a company called Prodrive Technologies. We explore how the company started with these AI platform and what it meant for the company to go from simple proof of concepts of applying machine learning or deep learning techniques to actually adding business value.

We listen to the journey that Thomas and his team had to take to understand, and then reveal all the common gotches around implementing MLOps. If you're interested in understanding the tools, the challenges, the story, the motivation, the obstacles that pro drivers overcome in the recent few years in this journey.

During this episode we mention a few neat packages:
-  MLFlow
- PyTorch
- TensorFlow
- Django
- anomalib

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In this episode, we talk to Pedro Holanda  who explains the motivation of why it's so useful to create a company around helping data scientists, use databases. In other words, people tend to use Pandas and CSV files a lot. And this sparked his curiosity alongside a few co-founder, of why is there not a better way of using databases for data scientists?

We explore how duckDB achieved  getting over 2 million downloads per month and how the secret source of making things as easy contributed to that success. 

We explore some of the technical differences between SQLite and duckDB, but also a bit of the technicalities of optimising storage memory and adaptation, talking of how to develop extensions and effectively. How does the DuckDB labs as a company got going  and where they're actually going in the future.

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In this episode, we explore Pluto. It is a Julia package, which enables people to ride Pluto notebooks. These notebooks are used by hundreds of thousands of people around the world to help explore the world around them. We talked to Fons to understand how he and some of his co-creators started this project, what motivated them to create yet another notebook framework and what are the key differences towards other notebooks, such as Jupiter, MATLAB Live editor, and maybe a little bit of Observable.

In addition to this, we will also explore how Pluto's are used in education with the Gerhard's experience as a teacher and a community manager, as the team collects a wide variety of use cases of where and how Pluto is used and how it helped people explore the world around. But not only do we understand and explore how it got going, how it's used today, we also take a sneak preview of what's to come in the future and understand how the community and the team make their decisions of what are the new features to be implemented and why. And it all revolves around some of their guiding principles that the team has put in place.   

Useful links that are referred in the episode

• https://featured.plutojl.org
• https://github.com/fonsp/Pluto.jl
• https://julialang.zulipchat.com/

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In this episode I interviewed Koen de Raad co-founder of https://eyedle.ai/ and we discussed how he and his team use Deep Learning programmed in Python in his company.

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In this short episode Gareth Thomas the host of Inspiring Computing will share the motivation this podcast. In short it answers the why, how, what, who and for whom this podcast is for.

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