Matthias Niehoff
@matthiasniehoff
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Real-World Data Architectures 💡 In his #SAGconf session, @matthiasniehoff shares insights from real projects, exploring various approaches to data architectures, the constraints they face, and the decisions that shape them. Learn how to apply these lessons to your own
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Auf zur #softwarearchitecturealliance! @matthiasniehoff erklärt, wo ihr anfangen könnt, wenn ihr die für euch passende Datenarchitektur sucht, @ufried spricht über resiliente IT & @Grinseteddy stellt Domain Storytelling und Event Storming vor: ➡️ https://t.co/A2PqzYVe6v
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🎙️ @matthiasniehoff is exploring the intersection of software engineering, data engineering, and machine learning. He compares whether these disciplines are fundamentally different or more similar than we think. #VoxxedDaysBrussels 🚀
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Announcing @matthiasniehoff as a speaker at #VoxxedDaysBrussels! 🚀 Explore the intersections between software engineering, data engineering and, machine learning and uncover valuable insights for both data and AI initiatives! See you soon! ➡️ https://t.co/y4Dwwg3ycM
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Announcement alert! 📢 @matthiasniehoff is set to take the stage at #VoxxedDaysBrussels! 🎙️Don't pass up the opportunity as we explore the topic: Can Data & ML really learn from Softare Engineering? Details here: https://t.co/ifXwmcCDrG Secure your spot for May 21st & 22nd! 🚀
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This is RAG Q&A with Claude, not a newly trained model - but I think us pedants may have decisively lost the battle to differentiate between training a model and building a RAG system at this point
Financial Times tests an AI chatbot trained on decades of its own articles
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To evaluate a tech announcement, ask yourself: 1. What can you do with this thing? 2. Are these capabilities new? 3. How do the new capabilities change the game? Do not ask: 1. Does it seem cool? 2. Does the tech seem complex / advanced? 3. Who's making it?
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#SoftwareEngineering, #Data und #MachineLearning: Völlig verschieden? Im Vortrag auf der @M3_Konferenz blickt unser Kollege @matthiasniehoffauf Gemeinsamkeiten und Unterschiede der Bereiche und zeigt, wie sie voneinander profitieren können. Zum Vortrag: https://t.co/WZcjnJA9w6
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Explore the rapidly evolving landscape of #ModernDataArchitecture at this year's #SAGconf! @matthiasniehoff will take a look into recent developments such as #DataMesh and #DataLakehouse, or the ELT pattern with you. Learn more: https://t.co/bDuXWdE7Fo
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YES! One can believe that LLMs can do amazing things and are useful, *without* believing they are anywhere close to human-level intelligence (even if they are superior to humans in a few tasks). One can believe that LLMs will give new tools to people with bad intention *without*
You can be amazed at Generative AI (and LLMs), while still recognizing their limitations. You can be concerned about Generative AI (and LLMs) opening up new attack surfaces, while still not stressing about fake threats. You can resist both hype and doom. Imagine!
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in the short window between now and AI wiping us out, would we at least have a brief period of time where we get to have self driving cars?
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👣 Reducing your #carbonfootprint while writing innovative #software: At @oop_conference digital, @ufried will present patterns of sustainability for a greener IT 🟢 https://t.co/7ZbcERtPxu
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"skill forward leads to cloud backwards" - a great talk by @timo_77833 about organizational challenges and mistakes in #cloud adoption. @codecentric #CloudLoveConference.
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hello to that one person who nods along encouragingly during presentations
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Interessante Diskussion. Wie so oft auf der @data2day dieses Jahr. Absolut gelungen. Spannende Teilnehmer, gute Talks und viel Raum zum Austausch in Person.
Was ist das Data Mesh? Nur ein Hype? Die Diskussion auf der @data2day ist eröffnet. @berndfondermann @jochen_christ @matthiasniehoff Stefan Kühn Dominik Benz
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Schon mal was von #Platform #Engineering gehört? Aktuell poppt das Thema an vielen Stellen auf, auch dank Tools wie @humanitec_com & #Backstage von @SpotifyEng. In seinem neuen Blogbeitrag gibt @dk_1977 eine Einordnung dazu: Jetzt lesen: https://t.co/isApDRPKpv
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Functions or containers? That is the question. Read @jonaspr1est's post for an evaluation of popular solutions such as #AWS #Lambda & #ECS with #Fargate. https://t.co/XmWOsQ2F8H
#faas #cloud #containers
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A lot of machine learning research has detached itself from solving real problems, and created their own "benchmark-islands". How does this happen? And why are researchers not escaping this pattern? A thread 🧵
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"Who hurt you?" "Well, I was the first data hire at..." "Let me stop you right there and give you a hug."
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A question I ask to prioritize data projects: 'Imagine you're done. It took 4 months. It works OK. Now, what metric has improved? By how much? Was it worth the effort & opportunity cost?' More on assessing impact from @jikechong & @yuec's book on leading in data science:
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