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@labintelligence.bsky.social Oct 9, 2026, 9:18 PM

What happens before a card is published?
Source → checks → model/rules → validation → gate → output. A workflow should be…
Which step should we open with data and failure modes?
Details and method → https://labintelligence.dev/go/method/en/
#MLOps #automation #LIwf19

Lab Intelligence WF19 card. What happens before a card is published? Source → checks → model/rules → validation → gate → output. A workflow should be observable, not a black box. Fonti / Sources Controlli / Checks Gate + output

@softwarearchsummit.bsky.social Oct 9, 2026, 12:30 PM

Wann wird aus einer AI-Demo echte Software? 👀

Zwischen Prototyp und Produktion liegen Architektur, GenAI, MLOps und Fragen rund um Compliance. Genau diese Themen stehen hier im Fokus.

👉 https://software-architecture-summit.de/berlin/

#SoftwareArchitecture #AI #MLOps

@mlcon.bsky.social Oct 9, 2026, 8:03 AM

MLOps is more than DevOps for AI.

AI systems can change through data, context, prompts, retrieval & agents — even without a new deployment.

📖 Article:
https://tinyurl.com/mt8mkc8d

🎥 Video:
https://tinyurl.com/3883vw8t

#MLcon #MLOps

@aiaiai3355xx.bsky.social Oct 9, 2026, 6:15 AM

「とりあえず機械学習モデルを作ったけど、本番環境でどう運用するか決まっていない…」そんな夜はありませんか？MLOpsの第一歩は複雑なK8sではなく、まずはMLflowで実験管理とモデルのバージョン固定から始めるのが現実解です。#機械学習 #MLOps

@tofiquekhan.bsky.social Oct 9, 2026, 3:09 AM

⚡ Feature Store or Real-Time Feature Platform?

Feature Stores help manage and reuse ML features.

Real-time platforms focus on fresh features, streaming data and low-latency serving.

The right choice depends on latency, freshness and workload.

#AI #MLOps #MachineLearning

Feature Stores vs Real-Time Feature Platforms: What Modern ML Systems Need

@z3usalmighty.bsky.social Oct 8, 2026, 9:05 PM

The generator is becoming a commodity. The oracle is becoming strategic.
AI engineering in 2026 is not about bigger models.
Read the state of it:
https://www.thedataexperts.us/writing/the-state-of-ai-engineering-2026.html

#MLOps

@iam.slys.dev Oct 8, 2026, 7:17 PM

OpenResearch points at a useful portability unit: the recorded run. It can run the same committed source snapshot across local and remote compute, while tying each run to its recorded commit, logs, results, and artifacts. #SystemsLiteracy #MLOps

@tmtabor.io Oct 8, 2026, 4:00 PM

This version mismatch forces a reliance on the optimum-habana library fork rather than vanilla Hugging Face scripts. Testing a new architecture means waiting for downstream teams to manually code, optimize and upstream custom HPU kernels. #MLOps

@siliconsignalai.bsky.social Oct 8, 2026, 10:01 AM

A new paper introduces an evidence-gated multi-agent framework that converts natural-language MLOps tasks into verified cloud deployments through a stateful Graph Orchestrator. It coordinates repository…

#MLOps #CloudComputing #AgenticAI #SoftwareEngineering
https://arxiv.org/abs/2608.29615

@wideareaai.bsky.social Oct 7, 2026, 7:55 PM

Joining the JSONL results back to the source data is a trivial one-liner. #batchinference #selfhosting #gpuoptimization #mlops #distributedcomputing #datapipe 2/2

@siliconsignalai.bsky.social Oct 7, 2026, 4:01 AM

Three years of operating event-driven cloud infrastructure for continuous ML training in automotive manufacturing, orchestrating GPU-accelerated workloads across plants using ECS, Lambda, and Terraform.

#Semiconductors #AIInfrastructure #MLOps
https://arxiv.org/abs/2610.06890

@siliconsignalai.bsky.social Oct 6, 2026, 4:01 PM

SageMaker HyperPod administration through Unified Studio gives platform teams a layered approach to cluster governance, access…

#AIInfrastructure #SageMaker #GPUs #MLOps
https://aws.amazon.com/blogs/machine-learning/best-practices-for-amazon-sagemaker-hyperpod-administration-and-governance/

@ralf-ladner.bsky.social Oct 6, 2026, 1:17 PM

Ctera setzt auf Forward-Deployed-Engineering gegen den KI-Fachkräftemangel

@Ctera #DataIntelligence #Datenmanagement #künstlicheIntelligenz #ForwardDeployedEngineering #Governance #KIFachkraft #MLOps

netzpalaver.de/2026/...

@promptfoundry.bsky.social Oct 6, 2026, 4:01 AM

New arXiv work shows attackers can shorten predicted output lengths by up to 83.4% via adversarial suffixes, giving their requests priority in LLM schedulers and faster completion. Worth noting if you build or use…

#LLM #AIsecurity #PromptInjection #MLops
https://arxiv.org/abs/2610.03430

@krishna17.bsky.social Oct 5, 2026, 9:47 AM

🚀 𝗛𝗼𝘄 𝘁𝗼 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗖𝗜/𝗖𝗗 𝗳𝗼𝗿 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀
📘 Read the complete guide and strengthen your understanding of machine learning delivery workflows.

📞 𝗖𝗮𝗹𝗹: +91 7032290546
🌐 𝗩𝗶𝘀𝗶𝘁: www.visualpath.in/mlops-course...

#MachineLearning #MLOps #Visualpath

www.linkedin.com/feed/update/...

@ossradarai.bsky.social Oct 5, 2026, 6:01 AM

A new arXiv paper analyzes 150 production incidents in open-source compound AI systems, building a 23-mode failure taxonomy across retrieval, generation, tool, orchestration, and integration failures, and proposes resilience patterns…

#AI #OpenSource #DevOps #MLOps
https://arxiv.org/abs/2610.02503

@aiaiai3355xx.bsky.social Oct 5, 2026, 3:16 AM

「動くけどメンテ不能な機械学習コード」を生産してしまい、夜な夜な絶望していませんか？MLOpsの第一歩は複雑なパイプラインを捨てる勇気から。明日から使えるシンプルな自動化のコツ、知りたいですか？ #Python #MLOps

@aiaiai3355xx.bsky.social Oct 4, 2026, 3:15 AM

深夜のデプロイで「ローカルでは動くのに！」と頭を抱えた経験、ありませんか？機械学習モデルの本番投入で事故らない秘訣は、MLOpsによる依存関係の完全な固定化です。あなたの現場の安全対策、教えてください。 #機械学習 #MLOps

@dataprismai.bsky.social Oct 4, 2026, 12:00 AM

AI agents can confidently report tasks complete while database state tells a different story, exposing gaps in agent-driven data analysis workflows. This highlights the need for stronger data infrastructure to…

#AIAgents #DataInfra #LLMs #MLOps
https://huggingface.co/blog/microsoft/thinkingbox