talk-data.com

Kirk Offel sits down with Daniel Thomas, CEO of GreenScale, one of Europe’s most ambitious new data center companies. In this candid conversation, Daniel shares his journey from a mechanical engineering student in London to leading large-scale data infrastructure projects across Europe. He dives into the meaningful vision behind GreenScale, exploring how their commitment to sustainability, renewable energy integration, and building a trusted team is helping them stand out in a rapidly evolving industry. For more about us: https://linktr.ee/overwatchmissioncritical

Hallo liebe Digital Bancassurance Community,

Wir laden Euch herzlich ein zu MeetUp #9!

Endlich ist es wieder soweit, unser nächstes Meetup steht an - diesmal in Berlin. Wir diskutieren mit Experten aus der Banken-, Versicherer- und Enabler-Welt die aktuellen Entwicklungen und Herausforderungen im Bereich Bancassurance.

Wann und wo? 🗓️ Dienstag, 06.05.2025, 18:00 Uhr 📍 My.B, Heidestr. 8, 10557 Berlin ✉️ Anmeldung bitte per E-Mail an [email protected] *

Die ersten Speaker und Beiträge stehen übrigens fest: 👉 „Ein Berliner Startup wird 112 Jahre!“

• Benjamin Westermann, Ideal Lebensversicherung a.G., Direktor Banken
• Andreas Ermisch, Ideal Lebensversicherung a.G., Direktor Banken

👉 „Bancassurance – Global Warming oder doch Cooling-off?“

• Thomas Alexander Jeske, HDI Deutschland, Geschäftsfeld HDI Bancassurance, Leiter Kooperationsentwicklung
• Daniel Bruch, LifeStyle Protection Versicherungen – eine Marke der HDI Bancassurance, Leiter Key Account Management

👉  „Next Level Digital Bancassurance – der datengestützte kanalübergreifende Blick auf den Markt hebt Banken und Versicherer auf ein neues Level“

• Dietmar Schmidt, CEO der mexxon Gruppe

Wir freuen uns auf spannende Diskussionen und den persönlichen Austausch mit Euch!

Euer Sebastian

*Alle E-Mail-Adressen werden selbstverständlich DSGVO-konform ausschließlich im Rahmen des Meetups verwendet.

Hello London Gophers! 👋

Welcome to the description page of another amazing Go event! Are you ready for the biggest Go event this side of the Thames?

📜 All London Gophers events operate under the Go Community Code of Conduct - https://golang.org/conduct

• Treat everyone with respect and kindness.
• Be thoughtful in how you communicate.
• Don’t be destructive or inflammatory.

Please do not message members without their consent

If you encounter an issue, please mail [email protected] or [email protected]

==== 📓 Agenda📓 =====

6:30 - 7:00pm: Arrival, Food & Refreshments

7:00pm: Talks Start

⚡️ Bruno Calogero - Project Gollum: Go & LLMs Document Question Answering (RAG) for production level Golang applications.

🗣️ George Thomas - Mono means one and lith means lith: how we've built our Go backend In a world eaten by best practice, we’re packing as much as possible into a single monolithic server. We’ll share the snippets, patterns, and libraries we’ve adopted — and those we wish we had — to make this work effectively. George is a former Google DeepMinder and Go Readability Mentor. He’s now the co-founder and CTO of Clusterfudge, which makes software for AI supercomputers.

🗣 Daniel Arves - Audio with Go? Heard of live-coding? Algoraves? Did you know there is a new(ish) platform written in pure Go with the standard library? If your answer to any of these is what the hell?! then I am here to help… An interesting creative application outside of the usual ecosystem.

\~8:30 - 9:00pm: Closing and Head to the Pub

==== 💡 Priority Queue 💡 =====

We now reserve 20% of the attendee spots at our events for those who are underrepresented in tech.

If they join the waitlist and there is a reserved spot open they will be bumped into going!

These spots are reversed until the last Sunday before the event.

How do we define underrepresented? We use public surveys done by the tech community such as the ones linked below.

https://survey.stackoverflow.co/2022/#section-demographics

https://www.jetbrains.com/lp/devecosystem-2022/#gender-and-development

==== 📢 Become a Speaker! 📢 =====

Have something to say? We want to listen! We are always looking for new speakers who want to share their adventures with Go and have mentors who can help.

You can sign up to be a speaker here: https://gophers.london/apply

==== 🎉 Prizes! 🎉 =====

JetBrains Raffle! - We have 3 free JetBrains Product licenses to giveaway to some of our lucky attendees.

==== 📝 Update Your RSVPS! 📝 =====

We monitor attendance and keep track of no-shows. Please if you can no longer make it to the event update your RSVP!

==== 📞 How To Reach Us 📞 =====

Email: [email protected] Linkedin: https://www.linkedin.com/company/london-gophers/ YouTube: https://www.youtube.com/c/LondonGophers

🎙️ Speaker: Daniel Lee\, Thomas Wiecki \| ⏰ Time: 16:00 UTC / 9am PT / 12pm ET / 6pm Berlin

This will be a high-level talk discussing the separation of statistical models and inference algorithms.

Things we’d like to talk about:

• The general vernacular combines two concepts together: model + inference. But they can be thought of separately.
• Given a statistical model, there are (at least) 3 different types of inference. Optimization, approximate inference, Bayesian inference. We’ll talk about some of the use cases of each. And where stochastic optimization fits in.
• A description of GPTs and how it can be implemented in Stan (and similarly in PyMC or any other PPL).

This talk won’t be overly technical. The goal will be to try to solidify the differences between the different types of inference and when to apply them. There will be plenty of time for Q&A.

📜 Outline of Talk / Agenda:

• 5 min: Intro to PyMC Labs and speakers
• 45 min: Presentation, panel discussion
• 10 min: Q&A

💼 About the speaker:

1. Daniel Lee Daniel Lee is at Zelus Analytics working on player projection models across multiple sports. Daniel is a computational Bayesian statistician who helped create and develop Stan, the open-source statistical modeling language with over 20 years of experience in numeric computation and software; over 10 years of experience creating and working with Stan; and 5 years working on pharma-related models including joint models for estimating oncology treatment efficacy and PK/PD models. Past projects have covered estimating vote share for state and national elections; clinical trials for rare diseases and non-small-cell lung cancer; satellite control software for television and government; retail price sensitivity; data fusion for U.S. Navy applications; sabermetrics for an MLB team; and assessing “clutch” moments in NFL footage. He holds a B.S. in Mathematics with Computer Science from MIT, and a Master of Advanced Studies in Statistics from Cambridge University.

🔗 Connect with Daniel Lee: 👉 LinkedIn: https://www.linkedin.com/in/syclik/ 👉 Twitter: https://twitter.com/djsyclik 👉 GitHub: https://github.com/syclik 👉 Website: https://syclik.com/ 👉 Blog: https://medium.com/@bayesianops

1. Dr. Thomas Wiecki (PyMC Labs) Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world-class team of Bayesian modelers and founded PyMC Labs -- the Bayesian consultancy. He did his PhD at Brown University studying cognitive neuroscience.

🔗 Connect with Thomas Wiecki: 👉 GitHub: https://github.com/twiecki 👉 Twitter: https://twitter.com/twiecki 👉 Website: https://twiecki.io/

📖 Code of Conduct: Please note that participants are expected to abide by PyMC's Code of Conduct.

🔗 Connecting with PyMC Labs: 👥 LinkedIn: https://www.linkedin.com/company/pymc-labs/ 🐦 Twitter: https://twitter.com/pymc_labs 🎥 YouTube: https://www.youtube.com/c/PyMCLabs 🤝 Meetup: https://www.meetup.com/pymc-labs-online-meetup/

🔗 Connecting with PyMC Open Source: 💬 Q&A/Discussion: https://discourse.pymc.io 🐙 GitHub: https://github.com/pymc-devs/pymc 💼 LinkedIn: https://www.linkedin.com/company/pymc/mycompany 🐥 Twitter: https://twitter.com/pymc_devs 📺 YouTube: https://www.youtube.com/c/PyMCDevelopers 🎉 Meetup: https://www.meetup.com/pymc-online-meetup/

Dask is a native parallel analytics tool designed to integrate seamlessly with the libraries you’re already using, including Pandas, NumPy, and Scikit-Learn. With Dask you can crunch and work with huge datasets, using the tools you already have. And Data Science with Python and Dask is your guide to using Dask for your data projects without changing the way you work! About the Technology An efficient data pipeline means everything for the success of a data science project. Dask is a flexible library for parallel computing in Python that makes it easy to build intuitive workflows for ingesting and analyzing large, distributed datasets. Dask provides dynamic task scheduling and parallel collections that extend the functionality of NumPy, Pandas, and Scikit-learn, enabling users to scale their code from a single laptop to a cluster of hundreds of machines with ease. About the Book Data Science with Python and Dask teaches you to build scalable projects that can handle massive datasets. After meeting the Dask framework, you’ll analyze data in the NYC Parking Ticket database and use DataFrames to streamline your process. Then, you’ll create machine learning models using Dask-ML, build interactive visualizations, and build clusters using AWS and Docker. What's Inside Working with large, structured and unstructured datasets Visualization with Seaborn and Datashader Implementing your own algorithms Building distributed apps with Dask Distributed Packaging and deploying Dask apps About the Reader For data scientists and developers with experience using Python and the PyData stack. About the Author Jesse Daniel is an experienced Python developer. He taught Python for Data Science at the University of Denver and leads a team of data scientists at a Denver-based media technology company. We interviewed Jesse as a part of our Six Questions series. Check it out here. Quotes The most comprehensive coverage of Dask to date, with real-world examples that made a difference in my daily work. - Al Krinker, United States Patent and Trademark Office An excellent alternative to PySpark for those who are not on a cloud platform. The author introduces Dask in a way that speaks directly to an analyst. - Jeremy Loscheider, Panera Bread A greatly paced introduction to Dask with real-world datasets. - George Thomas, R&D Architecture Manhattan Associates The ultimate resource to quickly get up and running with Dask and parallel processing in Python. - Gustavo Patino, Oakland University William Beaumont School of Medicine

“The way the information is presented appeals to teachers, hobbyists, web designers—anyone looking for a way to enhance their content by using customized maps.” —Warren Kelly, Pastor “It could become the de-facto tutorial volume for the subject, as well as the classic reference guide.” —Thomas Duff, Lead Developer “This book is written so well and is so easy to follow it’s a joy to go through.” — Daniel McKinnon, Software Engineer KML began as the file format for Google Earth, but it has evolved into a full-fledged international standard for describing any geographic content—the “HTML of geography.” It’s already supported by applications ranging from Microsoft Virtual Earth and NASA WorldWind to Photoshop and AutoCAD. You can do amazing things with KML, and this book will show you how, using practical examples drawn from today’s best online mapping applications. Drawing on her extensive experience with the creators of KML, Wernecke teaches techniques that can be used by everyone from programmers to real estate agents, scientists, students, architects, virtual explorers, and more. Highlights include Incorporating rich content in Placemark balloons Creating overlays that superimpose your images on standard Earth browsers Generating animations that move through Placemarks, Overlays, and Models Controlling and updating map content across the Web Managing large data sets using regions and custom data types Complete KML language reference: elements, types, syntax, file structure, and conventions