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#MLOps

2 posts2 participants0 posts today
Continued thread

1️⃣ 𝗞𝘂𝗯𝗲𝗳𝗹𝗼𝘄 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺: Navigating the Cloud-Native AI/ML and LLMOps Frontier
2️⃣ 𝗡𝗮𝘃𝗶𝗴𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗥𝗮𝗽𝗶𝗱 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗼𝗳 𝗟𝗮𝗿𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲: Where does Kubernetes Fit?
3️⃣ 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗧𝗔𝗚 𝗪𝗼𝗿𝗸𝗹𝗼𝗮𝗱𝘀 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻: Advancing the Core of Cloud Native Execution

github.com/terrytangyuan/publi

CNCF Kubernetes #CNK8sAIDay #KubeCon #CloudNativeCon #MLOps #AI #DevOps #Kubernetes #K8s #CloudNative

🏗️ Wusstet ihr, dass eure Software-Architektur aussieht wie euer Organigramm? Conway's Law zeigt: Teams, die nicht miteinander reden, bauen auch keine integrierten Systeme!

🤯 Besonders spannend bei ML-Pipelines und verteilten Teams - da wird's richtig wild!

Was sind eure Erfahrungen mit Team-Strukturen und System-Design? 💬

goern.substack.com/p/research-

Christoph Görn · Research Report: "Why Your Architecture Decisions Are Actually People Decisions"By Christoph Görn

Getting Started with Docker Model Runner 🚀🚀🚀

I created a step-by-step guide to getting started with Docker Model Runner (DMR).

The DMR - a new Docker Desktop 🐳 feature that enables the run of Large Language Models natively with Docker Desktop

By the end of this tutorial, you will be able to:
✅ Enable Docker Model Runner
✅ Pull LLMs from Docker Hub
✅ Run LLMs locally using both the CLI and Docker Desktop UI

Tutorial level: Beginner

🔗: theaiops.substack.com/p/gettin

The AIOps Newsletter · Getting Started with Docker Model RunnerBy Rami Krispin
#Docker#AI#MLOPS

Getting started with Docker Offload 🐳 +☁️ = 🚀

Docker announced Docker Offload last week, a new managed service that seamlessly integrates the local containers development workflow with a cloud-powered environment.

It is straightforward to set up and start a session with this new feature. I created a short tutorial to get started with Docker Offload 👇🏼

theaiops.substack.com/p/introd

📌 Subscribe to the AIOps newsletter to get updates 🗞️

The AIOps Newsletter · Introduction to Docker OffloadBy Rami Krispin

I am excited to launch my new newsletter - The AIOps 🥳

This newsletter is going to focus, as the name implies, on AI/ML Ops.

The newsletter format consists solely of hands-on tutorials, and the plan is to use this platform as a baseline for a future book 📚. The focus in the coming weeks is on Docker and its applications for data science and AI.

📌 Subscribe over here -> theaiops.substack.com/

theaiops.substack.comThe AIOps Newsletter | Rami Krispin | SubstackHands-on guides to the world of MLOps and AIOps. Click to read The AIOps Newsletter, by Rami Krispin, a Substack publication with hundreds of subscribers.
#AI#cicd#mlops

Next Thursday the 10th July is our 3-year Anniversary of running the #MLOps Meetup in Edinburgh! Stefano Bosisio and myself are really looking forward to hearing our two guests, Pat Wang and Mark Mc Naught talk on practical applications of GenAI and Agents: lu.ma/w5545zei

lu.maMLOps Meetup July 2025 (Two talks!) · LumaTonight (on our 3 Year Anniversary!) we’ll be hearing from: Pat Wang on “MLOps Meets GenAI: Evolving AI Deployment for the Generative Era". From Pat: Mark Mc…

🌟 Beginner Tutorial Alert at #EuroSciPy2025:

Deploy your Machine Learning model with FastAPI
📣 With Cheuk Ting Ho

Learn to:
🔹 Build an API for your pre-trained ML model
🔄 Design retraining workflows
🪄 Move from prototype to production-ready service
🛠️ Package in Docker & deploy to the cloud

Perfect for data scientists new to FastAPI or ML deployment.

📍 Kraków | 🗓️ August 18–22
🗓️ euroscipy.org/schedule

euroscipy.orgEuroSciPy 2025The EuroSciPy meeting is a cross-disciplinary gathering focused on the use and development of the Python language in scientific research.

🐍 New blog post: “Python, AI, and the MLOps Tinkerer's Toolkit”
A friendly guide to Python libraries for AI/ML — complete with pip install commands, PyPI links, and use cases from data wrangling to fine-tuning.
We love Python 💜
laurahargreaves.com/python-ai-

Laura Hargreaves · 🐍 Python, AI, and the MLOps Tinkerer's Toolkit
More from Laura Hargreaves 👩‍💻

First post! Sharing a side project I built: GPUprobe - a zero-instrumentation CUDA runtime monitoring tool using eBPF to detect memory leaks and track kernel launch frequencies in real time and expose them through a dashboard like Grafana. Inspired by BCC which once saved my ass debugging a nasty memory leak. Curious if anyone's working on GPU observability or AI infra, keen to swap ideas.

Check it out on github: github.com/GPUprobe/gpuprobe-d

The rise of low-code MLOps platforms creates a surprising twist: a new skills gap emerges! 🤯

Instead of lowering the bar, it's shifting the needed expertise.

This means DevOps/database skills are suddenly *super* valuable in MLOps.

What are your thoughts on the future of MLOps talent acquisition?

Continued thread

Many thanks to everyone who came along to the #MLOps Meetup last week, and also huge thanks to the presenters, Csoban Balogh and Danny Boland! I was particularly pleased that 40+ people turned up given the (unusually) warm weather 😀