Administered by:
#MLOps
AI agents with powerful hands
?
Yeah - but I’m not letting them wreck my system.
That’s why I use Docker: clean, safe, scalable.
Containers on fleek 
DevOps girl approved.
Watch the magic: https://youtube.com/shorts/tVFl4lFmRDQ
A surprising truth: Many ML infrastructure interviews prioritize technical skills over business understanding! 
This disconnect between tech and business goals in AI hiring is creating a significant talent gap.
What steps can the #AI community take to ensure future AI engineers are business-savvy?
Poll for AI builders:
𝗪𝗵𝗮𝘁’𝘀 𝘄𝗲𝗶𝗴𝗵𝗶𝗻𝗴 𝗵𝗲𝗮𝘃𝗶𝗲𝘀𝘁 𝗼𝗻 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮-𝗽𝗿𝗶𝘃𝗮𝗰𝘆 𝗺𝗶𝗻𝗱 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄?
(Pick one — feel free to elaborate in the replies.)
Ever wonder how AI actually makes things happen?
I broke down how Docker MCP works - clean, simple, and actually makes sense 
For my fellow DevOps & infra girls (and guys too 
)
Watch: 
Ever wonder what really powers AI behind the scenes?
I’m breaking down Docker MCP — where agent servers & a host app let models take action.
DevOps girlies, this one’s for you 
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 -> https://theaiops.substack.com/
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: https://lu.ma/w5545zei
AI can code, debug, even write poetry...
But ask it to post in Slack? "Sorry babe, I just talk."
Let’s talk DevOps, infra, & what actually powers smart AI.
Watch my Short & level up: https://youtube.com/shorts/e5hPmK2FX7c
Tried vibe coding in real ML workflows: from EDA to model deployment.
What worked? What failed? Find out here: https://softwaremill.com/is-vibe-coding-ml-a-viable-approach/
#AI #MachineLearning #OpenSource #Cursor #MLOps #VibeCoding
Staging went down. CI 
. No errors. Just silence.
The root cause? A single Docker tag: latest
AI infra quietly broke behind the scenes.
Stop gambling with prod.
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 
https://laurahargreaves.com/python-ai-ml/?utm_source=mastodon&utm_medium=social&utm_campaign=autopost
How to use Apache Airflow for MLOPS and ETL - Description, Benefits and Examples:
https://www.glukhov.org/post/2025/06/apache-airflow/
#python #coding #mlops #devops #opensource #machinelearning
Is your obsession with ML accuracy killing your budget? 
The highest-performing model isn't always the "best" one. A slightly less accurate model that's 10x cheaper and faster to run often delivers massively more business value.
We're trapped chasing a single metric while ignoring the total cost of ownership. It's time to prioritize the cost-performance ratio.
#BeyondAccuracy #MLOps #AI #CostPerformance #ROI #Tech
Read the full breakdown here: https://link.illustris.org/bgqFLG
"It works on my machine."
CI is on fire. Bugs haunt your builds.
Only fails on macOS... during a full moon.
Enter Docker. Same env. Same result.
DevOps peace achieved.
ML 성능의 진짜 병목, 모델 밖에서 찾아라
느린 Spark 파이프라인 대신 Ray를 ML 전체에 적용한 핀터레스트. 데이터 백필 없이 즉시 피처를 생성하고 결합하여 실험 반복 속도를 10배나 단축했습니다.
진짜 ML 성능 향상은 모델 알고리즘 개선이 아닌 데이터 처리 과정의 혁신에서 비롯됩니다.
#MLOps #데이터엔지니어링 #머신러닝 #파이프라인최적화 #성능개선
https://medium.com/pinterest-engineering/scaling-pinterest-ml-infrastructure-with-ray-from-training-to-end-to-end-ml-pipelines-4038b9e837a0
AI isn’t just smart — it moves 
With Docker + agents, your models go from prompt 
production
Fast. Secure. Cloud-native. No hacks, just clean infra 
DevOps meets AI — watch it happen:
A surprising truth: Many ML infrastructure interviews prioritize technical skills over business understanding!
This disconnect between tech and business goals in AI hiring is creating a significant talent gap.
What steps can the #AI community take to ensure future AI engineers are business-savvy?
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: https://github.com/GPUprobe/gpuprobe-daemon
