Inspiring Computing

In this podcast, Abel discusses his transition from academia and Brazil to becoming a research software engineer in the Netherlands. He emphasizes his reasons for choosing Julia such as its package ecosystem and speed. Abel details, his involvement with the Netherlands eScience Center, where he collaborates with researchers to develop software solutions. He also covers his background in applied mathematics and his early adoption and advocacy of Julia. The conversation touches on Julia Smooth Optimizers its utility and optimizing non-linear problems. And the importance of benchmarking for development. Abel shares insights into maintaining multiple interdependent packages in the Julia. And underscores the role of automation in testing and documentation. You also mentioned his contributions to the Juliet community through YouTube. And expresses openness to collaborating and supporting both academic and industrial projects involving Julia.

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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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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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