Agenta | Cossmology Open-source LLMOps platform

Agenta - Open-source LLMOps platform

Cossmology Profile: https://dub.sh/s00LukP

Key People: Mahmoud Mabrouk (@mahmoudmabrouk.bsky.social), Akrem Abayed

#LLMOps #OpenSource #OSS #COSS

Cursor、リアルタイム強化学習で「Composer」を高速改善

【実は...】CursorのComposer機能、さらに進化してた。

リアルタイム強化学習で、コード生成速度と精度を継続的に改善してるらしい。AIエージェントが「使われるほど賢くなる」ループ、これが開発現場で実装されるとエグいね。

これ、皆さんの環境でも実感ある？🤔

#AI #Cursor #AIエンジニア #LLMops

https://codezine.jp/news/detail/23805

AIコーディングエージェントを2日間使い倒してみた──Quota消費の実態レポート｜Sea はじめに 最近「Antigravity（アンチグラビティ）」というAIコーディングエージェントをガッツリ使っています。VS Codeに統合されていて、AIと一緒にコードを書いたり、バグを直したり、アプリをデプロイしたりできるツールです。 今回は2日間（2026年3月28〜29日）集中して使った結果、Quotaがどれだけ消費されたのかを実データと共にご紹介します。非エンジニアがVibe Codingでどんな作業をしたのかも詳しく書きます。 Antigravityとは？ AntigravityはGeminiやClaude、GPTなど複数のAIモデルを切り替えながら使えるVS Co

AIコーディングエージェント「Antigravity」を2日間使い倒した実録レポートが興味深い。

特に「データベース移行（SQLite→GAS）」がQuotaを最も消費する要因という分析は、自律エージェント運用時のコスト管理において非常に参考になる。

皆さん、エージェント運用のQuota管理はどうしてますか？

https://note.com/quai_hound3532/n/ne49cb38a495d

#AI #Antigravity #LLMops #開発効率

【検証】Whisperの推論パイプラインを「BatchedInferencePipeline」に変更するだけで、6GB VRAM環境の制限が劇的に改善。
3時間以上の音声でOOM落ちていたのが、9時間分でも7分で処理完了。
Whisper活用してる人はコード修正の価値あり。 #AIエンジニア #LLMops #Python

AI Agent Platforms Compared: From Enterprise to Self-Hosted - Cuttlesoft, Custom Software Developers Compare nine AI agent platforms from OpenAI Frontier to self-hosted Dify. A practical guide to choosing the right deployment infrastructure for your agents.

We compared enterprise agent platforms to self-hosted options. The gap is closing fast, but vendor lock-in on tool integrations is where the real cost hides.

cuttlesoft.com/blog/2026/02/24/ai-agent...

#AIAgents #LLMOps

Universal LLM API that makes switching between GPT-4, Claude, Bedrock, and 100+ other AI models as s

Universal LLM API that makes switching between GPT-4, Claude, Bedrock, and 100+ other AI models as simple as changing one parameter - with built-in cost tracking and load balancing

https://github.com/BerriAI/litellm

#LLMOps #AIGateway #OpenAI

Week of March 23, 2026  ·  Early-stage AI intelligence for eLab Ventures

If you’re still arguing about models in 2026, you’re focused on the wrong layer. This is the Year of the Claw.
#AgenticSystems #AIInfrastructure #LLMOps #AIAgents #EnterpriseTech #DataStrategy #Automation #AppliedAI #OpenClaw #NemoClaw

bit.ly/3PioNlp

Awakari App

Build a Complete LLMOps Pipeline with MLflow and Google Gemini — Part 1 Learn how to build full request tracing, multi-turn session grouping, and human feedback into your GenAI app using MLfl...

#llmops #machine-learning #mlflow #llm #generative-ai

Origin | Interest | Match

Hugging Face x dltHub: The missing data layer for ML From raw data to production ML: load, transform, embed, and publish curated datasets with declarative pipelines powered by dltHub.

Thanks to @lhoestq.hf.co and the @hf.co datasets team for the collaboration.

Launch blog:
https://dlthub.com/blog/hugging-face-dlt-ml

#DataEngineering #MachineLearning #LLMOps

Architectural Foundations of MLOps, AIOps, and LLMOps A Practical Production Blueprint for Modern AI Systems Most people think building the...

Architectural Foundations of MLOps, AIOps, and LLMOps A Practical Production Blueprint for Modern AI Systems Most people think building the model is the hard part. It isn’t. Training a model in a...

#mlops #aiops #llmops #ai

Origin | Interest | Match

Your Action:
Pick one AI tool you use. Ask:
• How would I know it's working?
• How track costs?
• How ensure no leaks?

Tomorrow: Day 7—API Integration Basics.
#30DayFutureProof #LLMOps #AI #TechSimplified

I Tried 15+ LLMOps Courses on Udemy: Here are My Top 5 Recommendations for 2026 credit — medium.com Hello friends, Large Language Models (LLMs) are redefining what’s possible with AI, but deploying them in real-world systems is where the real challenge begins. That’s where LLMOps comes in — — the discipline of operationalizing LLMs at scale, managing everything from fine-tuning and optimization to versioning, monitoring, cost control, and serving in production. > It’s MLOps on steroids, built for the unique needs of foundation models. In 2026, the demand for AI engineers and ML practitioners who can not only fine-tune but also deploy and manage LLMs in production has exploded. Whether you’re building your first GPT-based app or trying to get Llama 3 running efficiently with quantization on GPU clusters, these Udemy courses will equip you with the right tools. If you’re serious about AI engineering and don’t want to be left behind as models grow more powerful and infrastructure grows more complex, this is your starting point. If you want to learn _LLMOps_ in 2026 and looking for best online resources then you have come to the right place. Earlier, I have shared best AI and Machine Learning courses, and Gen AI and LLM courses and today I am going to share best online courses from Udemy to learn LlamaIndex in 2026. While books like _AI Engineering by Chip Huyen_ _and_ _The LLM Engineering Handbook by Paul Iusztin and Maxime Labonne_ are a good starting point, but if you really want to gain confidence, nothing beats learning by doing — — and that’s where these Udemy courses shine. ## AI Engineering: Building Applications with Foundation Models ### AI Engineering: Building Applications with Foundation Models [Huyen, Chip] on Amazon.com. *FREE* shipping on qualifying… www.amazon.com ## LLM Engineer's Handbook: Master the art of engineering large language models from concept to… ### LLM Engineer's Handbook: Master the art of engineering large language models from concept to production [Iusztin, Paul… www.amazon.com ## 6 Best Udemy Courses to Learn LLMOps in 2026 Without any further ado, here are the best online courses you can join on Udemy to learn how to deploy large language models in production also known as LLMOps. ### 1. Deploying LLMs: A Practical Guide to LLMOps in Production This is one of the most current and comprehensive guides specifically focused on LLMOps. The course explores model deployment using Llama 3, GPT, LoRA, AWQ, GPTQ, and production-ready practices with Ray, MLflow, and Flash Attention. You’ll learn how to manage compute costs, optimize model loading, and implement scalable deployment patterns. If you want to get serious about deploying open-source models or fine-tuned LLMs at scale, start here. Here is the link to join this course — — Deploying LLMs: A Practical Guide to LLMOps in Production ### 2. 2026 Deploy ML Model in Production with FastAPI and Docker HuggingFace Transformers, FastAPI, Docker, and AWS — — this course combines them all. You’ll deploy ViT, BERT, and TinyBERT models in real-world cloud environments. The focus is on packaging and serving models in a secure and scalable way. Even though it’s not LLM-specific, the techniques covered here apply directly to building reliable backend services for LLM applications. Here is the link to join this course — — 2026 Deploy ML Model in Production with FastAPI and Docker ### 3. LLMOps Masterclass 2026 — Generative AI, MLOps, AIOps If you’re looking to understand how LLMOps fits within MLOps and AIOps, this is your course. It provides a broader perspective on managing generative AI systems beyond just deployment. You’ll get hands-on experience deploying HuggingFace and OpenAI models with a focus on monitoring, cost optimization, and automation pipelines. A must if you want to think beyond one-off deployments. Here is the link to join this course — — LLMOps Masterclass 2026 — Generative AI, MLOps, AIOps ### 4. Complete MLOps Bootcamp With 10+ End To End ML Projects Students: 22,612 (Bestseller) Why take it: If you prefer project-based learning, this bootcamp delivers 10+ end-to-end real-world machine learning projects — — from data prep and training to deployment and automation. While LLMs are not the only focus, the course builds your foundational MLOps skills, which are essential before moving to LLMOps. It’s a strong fit for engineers transitioning into AI infrastructure roles. Here is the link to join this course — — Complete MLOps Bootcamp With 10+ End To End ML Projects ### 5. Azure AI Studio (AI Foundry): Prompt Flow, LLMOps & RAG Students: 2,271\ Why take this course: If you work in a Microsoft Azure environment, this course is for you. It focuses on Prompt Flow, RAG (Retrieval-Augmented Generation), and other Azure-native LLMOps tools. It covers model evaluation, content safety, and LLMOps workflows in Azure AI Studio, making it a good option for enterprise engineers or teams deploying AI apps inside Microsoft’s cloud ecosystem. Here is the link to join this course — — Azure AI Studio (AI Foundry): Prompt Flow, LLMOps & RAG ### 6. Deploying AI & Machine Learning Models for Business | Python Students: 9,902 Why take it: This course focuses on business-ready model deployment. It shows how to build ML, deep learning, and NLP applications and wrap them with Docker containers for real-world deployment. Although not LLM-centric, it’s highly relevant for engineers who need to deploy LLM pipelines as part of broader AI workflows — — especially useful for Python developers coming from a traditional ML background. Here is the link to join this course — — Deploying AI & Machine Learning Models for Business | Python ## Why Learn LLMOps in 2026? Language models have gone from research tools to production-critical systems. But deploying them isn’t as simple as calling an API. LLMs are compute-hungry, dynamic, and often need custom datasets, fine-tuning, and orchestration. As organizations adopt them across search, chatbots, agents, and more, LLMOps becomes essential to ensure: * Scalability without breaking the bank * Monitoring to avoid hallucinations or failures * Version control for fine-tuned checkpoints * Security and compliance for enterprise use * Toolchain integration with platforms like Ray, LangChain, MLFlow, Azure, HuggingFace, etc. Companies are actively hiring LLMOps engineers and specialists to manage this complexity. If you want to future-proof your career in AI, investing in LLMOps is one of the smartest decisions you can make this year. _year._ That’s all about the top 6 Udemy courses to learn LLMOps in 2026. Mastering LLMOps and learning how to deploy language models in production isn’t just a nice-to-have skill anymore — — it’s essential for anyone serious about working with AI at scale. The courses we’ve explored offer hands-on guidance, real-world projects, and the technical depth you need to bridge the gap between experimentation and production. Whether you’re deploying models with FastAPI, fine-tuning LLaMA 3, or integrating with Azure AI Studio, these resources equip you to build reliable, efficient, and scalable AI systems. Invest the time to learn these tools properly — — you’ll thank yourself when your models move seamlessly from prototype to production. By the way, if you want to join multiple course on Udemy, its may be worth getting a Udemy Personal Plan, which will give instant access of more than 11,000 top quality Udemy courses for just $30 a month. If you got a lot of time and want to save money, _Udemy Personal Plan_ will be perfect for you. Other AI, LLM, and Machine Learning resources you may like * Top 5 Courses to Prepare for AIF-C01 Exam in 2026 * 16 System Design Resources for Software Engineers * 10 Best Udemy Courses to learn Artificial Intelligence in 2026 * 5 Best Udemy courses to learn Midjourney in 2026 * 6 Udemy Courses to learn AWS Bedrock in 2026 * How to Prepare for AWS Solution Architect Exam in 2026 * Top 5 Udemy courses to build AI Agents in 2026 * 7 Best Courses to learn AWS S3 and DynamoDB in 2026 * Top 5 Udemy Courses for AWS Cloud Practitioner Exam in 2026 * 5 Best Courses to learn AWS SageMaker in 2026 * 8 Udemy courses to learn Prompt Engineering and ChatGPT * 5 Best Udemy Courses to learn Building AI Agents in 2026 * Top 5 Udemy Courses to learn Large Language Model in 2026 Thanks a lot for reading this article so far, if you like these best LLMOps courses on Udemy then please share with your friends and colleagues. If you have any feedback or questions then please drop a note. > P. S. — — If you want to learn from books and looking for best AI and LLM Books then I highly recommend you to read AI Engineering by Chip Huyen and The LLM Engineering Handbook by Paul Iusztin and Maxime Labonne, both of them are great books and my personal favorites. They are also highly recommend on Redditt and HN. ## AI Engineering: Building Applications with Foundation Models ### AI Engineering: Building Applications with Foundation Models [Huyen, Chip] on Amazon.com. *FREE* shipping on qualifying… www.amazon.com

I Tried 15+ LLMOps Courses on Udemy: Here are My Top 5 Recommendations for 2026 credit — medium.com Hello friends, Large Language Models (LLMs) are redefining what’s possible with AI, but deplo...

#artificial #intelligence #courses #DevOps #LLM #LLMOps #Udemy

Origin | Interest | Match

Awakari App

Foundations of LLM Inference Optimization: Understanding Mixed Precision and Quantization | Part 3C How Do We Store Fewer Bytes Without Breaking Accuracy? Continue reading on Medium »

#ai #llmops #machine-learning #llm #data-science

Origin | Interest | Match

In MLOps, you monitored model drift.
In the Agent Era you monitor decisions.
An agent reasons, calls tools & retrieves context across multiple steps. Any step can fail silently.
Your old MLOps playbook didn't break. The problem just changed shape.
#AIObservability #LLMOps #MLOps

OKRs for AI Agents: Congratulations, You’ve Rebuilt Management Why “agent autonomy” won’t kill bureaucracy — it will just rebuild it in JSON, evals, and ruthless feedback loops.

We didn’t kill management with AI agents. We rebuilt it in JSON, evals, retries, and orchestration graphs.
go.abvx.xyz/qsvqls
#AgenticAI #LLMOps #AIOps #WorkflowDesign #OrgDesign

AI agents love hallucinating company facts.
Feed them structured company context instead.
Yoku has an MCP server ready to plug in.

See MCP: https://yoku.app/features/mcp
#AI #MCP #LLMOps

Awakari App

3 Steps to Distill LLMs: Shrink Your Model and Save Money Chinese AI labs like DeepSeek and Moonshot didn’t invent distillation, but they showed the world what it can do. They built models that...

#llm #llmops #mlops #distillation #machine-learning

Origin | Interest | Match

LLMOps Is the New DevOps: AI in 2026 AI-first teams are scaling fast, but DevOps wasn’t built for probabilistic systems. Discover why LLMOps is essential for governing AI products in 2026.

As AI experiences shift from experiments to mission‑critical infrastructure, LLMOps brings the operational discipline needed to run large language models at scale

Read more 👉 www.valuecoders.com/blog/ai-ml/l...

#LLMOps #AI #DevOps #MachineLearning #AIOps #EnterpriseAI #TechTrends

GitHub - uogbuji/Anya: Simple, headless AI agent runner: scheduled jobs, skills/flows, email reports, activity blotter. Read-only intent—Not trying to be OpenClaw here. Simple, headless AI agent runner: scheduled jobs, skills/flows, email reports, activity blotter. Read-only intent—Not trying to be OpenClaw here. - uogbuji/Anya

Anya. Been noodling as I pondered #OpenClaw. Defo for tinkerers, but alr very useful for me. Ideas: 1) Clarity of short-branch DAG flow 2) Separation of deterministic from ND blocks 3) Simple UNIX spirit. Local #LLM provider support
github.com/uogbuji/Anya
#GenAI #LLMOps #AgenticAI #Agent

Token caching + cost transparency may sound wonky, but they’re make-or-break for creators scaling AI. Frux is tackling both with some smart tooling. Worth a look: https://frux.pro/

#AI #LLMOps #GenAI

Awakari App

Notebook Agents Are Toys Day 27 of 30: The runtime is the product Continue reading on Medium »

#llmops #agentic-ai #kubernetes #design-systems

Origin | Interest | Match

Awakari App

8 Best Udemy MLOps Courses for 2026 (Tested & Ranked) Looking for the best Udemy MLOps courses in 2026? We tested and ranked 8 top-rated courses covering MLflow, Docker, Kubernetes, AWS… Cont...

#mlops #machine-learning #llmops #devops #ai

Origin | Interest | Match

Original post on pyimagesearch.com

Vector Search Using Ollama for Retrieval-Augmented Generation (RAG) Table of Contents Vector Search Using Ollama for Retrieval-Augmented Generation (RAG) How Vector Search Powers Retrieval-Augmente...

#AI #& #Machine #Learning #LLMOps #Natural #Language […]

[Original post on pyimagesearch.com]

Original post on pyimagesearch.com

Vector Search Using Ollama for Retrieval-Augmented Generation (RAG) Table of Contents Vector Search Using Ollama for Retrieval-Augmented Generation (RAG) How Vector Search Powers Retrieval-Augmente...

#AI #& #Machine #Learning #LLMOps #Natural #Language […]

[Original post on pyimagesearch.com]

aks-demos/aks-kaito at main · RoyKimYYZ/aks-demos Azure Kubernetes Demos for the purposes of sharing my knowledge to the technology community - RoyKimYYZ/aks-demos

Want to run LLMs on AKS without the infrastructure headache? 🚀
Check out my repo: 🔗
github.com/RoyKimYYZ/ak...
✅ Automated GPU provisioning
✅ One-click OSS model deployment (Phi, Llama, Falcon)
✅ Private & secure inference endpoints
#AKS #Kubernetes #Azure #GenerativeAI #LLMOps #KAITO #mvpbuzz

AI tools are powerful—but costs can blow up fast. Frux cuts waste with token reuse and smarter workflows, helping keep AI projects sustainable. https://frux.pro/

#AI #LLMOps #DevTools

Engy Fouda (@engyfouda) Part 4–Agents in the Wild (Agentic AI, Tools, Orchestration, and Governance) Part 4–AI Articles and Tutorials Series #AI #LLM #AI_Agent #RAG #LLMOps https://datascienceinaction.substack.com/p/part-...

Part 4–Agents in the Wild (Agentic AI, Tools, Orchestration, and Governance)

Part 4–AI Articles and Tutorials Series

#AI #LLM #AI_Agent #RAG #LLMOps

datascienceinaction.substack.com/p/part-4agen...

Maybe it's too early. Maybe it's competitive advantage. Maybe everyone's just using API providers and not thinking about it yet.

If you're running heavy agents at scale, I'm genuinely curious: how are you handling this?

#AIAgents #MLInfra #LLMOps #GPUScheduling

Mastering LLM Operations: Unleashing the Power of Amazon Bedrock
#AmazonBedrock #LLMOps #AIonAWS #GenerativeAI #AIOperations #LLMEngineering #BedrockModels #CloudAI #ScalableAI #AWSInnovation #AITechnology #FutureOfAI #AIIntegration #MLSolutions #TechTransformation

Mastering Large Language Model Operations with Amazon Bedrock As artificial intelligence continues to evolve, the capability to harness large language models (LLMs) has become crucial for businesses seeking to leverage AI-driven solutions. Amazon Bedrock, a f...

Mastering Large Language Model Operations with Amazon Bedrock
www.ekascloud.com/our-blog/mas...
#AmazonBedrock #LLMOps #GenerativeAI #AIEngineering #CloudAI #BedrockAI #AIOperations #LLMDeployment #AWSAI #FutureOfAI #MachineLearningOps #AIInnovation #ScalableAI #CloudComputing #AIDrivenSolutions