#ITByte: #LLMOps, or Large Language Model Operations, is a set of practices and tools that speed up the development, deployment, and management of AI models.

It's a subset of Machine Learning Operations (#MLOps) tools that focuses on large language models (LLMs) and their unique challenges.

https://knowledgezone.co.in/trends/explorer?topic=LLMOps

️ Multiple critical deserialization vulnerabilities in PyTorch Lightning could lead to remote code execution when loading model files.

https://socket.dev/blog/pytorch-lightning-deserialization-vulnerabilities #MLops #PyTorch #AIsecurity

Lisbon AI folks!

Excited to see many of you tomorrow for our AI Agents meetup with the #MLOps Community and Lumiera at Cloudflare Lisbon.

We’ll talk about:
Domain-specific agents
Deployment challenges
Real-world lessons from teams building and shipping

Speakers from Mozilla.ai, Tripadvisor, and Unbabel.

Snacks, drinks, and good conversation await!

Don’t forget to confirm via Verkada if you RSVP’d.

Details: https://lu.ma/hmytvzm3

It's less than a month till the next #MLOps Edinburgh Meetup, where we'll be hearing from Danny Boland talking about “Scaling an LLM-Based Product Feature at Zoe": https://lu.ma/2spfbnk8

Master Gen AI for DevOps with Industry Experts!
Join our FREE Demo Session on April 5th, 2025, at 9:00 AM (IST)!
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Circuit tracing opens the black box of language models. A breakthrough for understanding how AI thinks. #AI #NLP #MLOps https://transformer-circuits.pub/2025/attribution-graphs/methods.html

Visualpath provides expert-led Gen AI For DevOps Course Online with a hands-on, practical approach. Designed by industry professionals, our online Generative AI For DevOps Training Pune equips you with real-world expertise. Available in Pune, Bangalore, Chennai, and worldwide, you can learn from anywhere.For more details, contact +91-7032290546
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MLOps: The Missing Link in Your Machine Learning Strategy

MLOps bridges the gap between data science and engineering, creating sustainable ML systems that actually work in the real world.

A proper MLOps workflow includes:
Automated data ingestion
Continuous model training
Performance monitoring
Drift detection
Seamless redeployment

https://link.illustris.org/mlopscode2prod

My second report from #SREcon: Some of the same lessons -- and unsolved problems -- from supporting #machinelearning apps in production carry over to #generativeAI apps, but not all. Attendees discussed the similarities and important differences. #reliabilityengineering #ML #GenAI #LLM #AI #MLOps #LLMOps https://www.techtarget.com/searchitoperations/news/366621071/Site-Reliability-Engineers-weigh-MLOps-vs-LLMOps

Looking forward to attending both #MLOps Meetup in London (https://lu.ma/ep/ep-7vSG3qWYoy) on Wed and then State of the Browser conference (https://2025.stateofthebrowser.com/) at the weekend!
If you know me, or even if you don't , feel free to give me a ping here if you wanna grab a chat over coffee/beer/lunch at either of these.

What does DeepSeek's "Open Source Week" mean for COSS?

#COSS #OSS #DeepSeek #AI #LLM #ML #MLOps #infrastructure

https://www.youtube.com/watch?v=QH0cJPS9ddI&list=PLr4nRUtZbsuGq-U9BCJoRvriJd9vZyhLN&index=8

Some of the talks in this year's #MLOps Community AI in Production UPDATED WITH PLAYLIST https://youtube.com/playlist?list=PL3vkEKxWd-uvXtxKLyod9UXeQzOVCYu1K&si=OFOzMj7MUTUtYx0v

1/6 Most ML projects never make it to production.

At #QConSF, Wenjie Zi (Senior Applied AI Engineer at Grammarly) shared why ML projects fail and how to improve your odds.

From business misalignment to data issues, here are key takeaways.

Rereading @eugeneyan's take-aways from the first AI Engineer Summit in 2023 and reflecting on the stuff that has changed in two years time. Multi-modal applications definitely picked up in 2024, and the large model providers have started to offer caching.

https://eugeneyan.com//writing/aieng-reflections/

Excited to share that I am speaking at KubeCon Europe in London next month! Looking forward to catching up with friends and collaborators! You can find me at the following sessions #KubeCon #CloudNativeCon #CloudNative #Kubernetes #DevOps #MLOps #AI #K8s @kubernetes.io #KServe #Kubeflow @cncf.io

An analysis of 100 Fortune 500 job postings reveals the tools and technologies shaping the data engineering field in 2025. Top skills in demand:
⁕ Programming Languages (196) - SQL (85), Python (76), Scala (14), Java (14)
⁕ ETL and Data Pipeline (136) - ETL (65), Data Integration (46)
⁕ Cloud Platforms (85) - AWS (45), GCP (26), Azure (14)
⁕ Data Modeling and Warehousing (83) - Data Modeling (40), Data Warehousing (22), Data Architecture (21)
⁕ Big Data Tools (67) - Spark (40), Big Data Tools (19), Hadoop (8)
⁕ DevOps, Version Control, and CI/CD (52) - Git (14), CI/CD (13), DevOps (7), Version Control (6), Terraform (6)
...

#DataEngineering #BigData #SQL #Python #ETL #AWS #CloudComputing #Spark #DataModeling #DataWarehouse #DevOps #DataGovernance #DataVisualization #MachineLearning #API #Scala #Java #GCP #Azure #Hadoop #Git #CICD #Terraform #DataQuality #Tableau #PowerBI #Collaboration #Microservices #MLOps #TechSkills

https://www.reddit.com/r/dataengineering/comments/1hz5ytw/become_a_data_engineer_in_2025_based_on_100_jobs/?utm_source=perplexity&rdt=54709