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What does an 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 look like? MLOps practices are there to improve Machine Learning Product development velocity, the biggest bottlenecks happen when Experimentation Environments

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What to learn as MLOps Engineer 🔥

A GitHub repo that contains everything related to MLOps. • Week 0: Project Setup • Week 1: Model Monitoring - Weights and Biases • Week 2: Configurations - Hydra • Week 3: Data Version Control - DVC • Week 4: Model Packaging - ONNX • Week 5: Model Packaging - Docker •

For those 9,999,999,999 boys and girls who get confused about Kubernetes components and what they do. Here I've simplified it for you. 55K+ read my DevOps and Cloud newsletter: https://t.co/UStCmOZImx What do we cover: DevOps, Cloud, Kubernetes, IaC, GitOps, MLOps 🔁

This Week in DevOpsCube👇 Get discounts, guides, use cases, & CKA Prep course practice tests! Don’t miss out! 🖥️ 𝗗𝗲𝘃𝗢𝗽𝘀 𝗕𝗹𝗼𝗴𝘀 & 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 1. How to get started with MLOPS? - https://t.co/TUCmF329Sj 2. Kubernetes Device Plugins - https://t.co/Tp3rE6GBep 3.

Do you know how the temperature parameter controls how creative or precise an LLM sounds? It’s one of the simplest — yet most dont know how it works behind the scenes . Let’s break it down #AI #AIAgents #MLOps #BuildInPublic

🟢Awesome MLSecOps - A curated list of MLSecOps tools, articles and other resources on security applied to Machine Learning and MLOps systems. 🚨 https://t.co/8JAlI4vOpI

A machine learning system is the complete infrastructure, or MLOps pipeline, that brings a model to life in the real world. It’s not just the trained model itself. It includes automated data pipelines for ingestion and cleaning, the training and validation infrastructure, and an

Built Observa — a full-stack #MLOps project for NLP inference with MLflow, DVC, Grafana & Prometheus integration. Everything tracked, versioned & observable.

if you wanna learn MLOps, i found this perfect roadmap from a senior ML engineer who’s actually built ML systems for companies. 📩 bookmark this. here is a full list of what you need to learn + resources and links:

【Codex + freqtradeで加速されたAI-MLOps】 本当に驚いている、怖ぐらいです。 トレード戦略のアイデアから実装、本番の展開までの速度が尋常ではないほどの速さ。これが段々と確信になる過程を体験できている。 将来、これがAI自身が研究アイデアを発見し、フルオートになることに恐怖を感じる。

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Many Cloud Engineers don’t fully understand the DNS routing policies and when to use them. Here, I’ve made this to help you better understand. 55K+ read my DevOps and Cloud newsletter: https://t.co/V3efGRUAgE What do we cover: DevOps, Cloud, Kubernetes, IaC, GitOps, MLOps 🔁

The "Too many concurrent requests" message proves that AI scaling infrastructure is the current bottleneck, turning a technical error into a strategic lesson on compute capacity and MLOps. #ChatGPT #ChatGPTdown #artificial_intelligence #technology #Futures #SamAltman

How to Structure Your Kubernetes Project !! Here, I’ve made this to help you better understand. 55K+ read my DevOps and Cloud newsletter: https://t.co/V3efGRUAgE What do we cover: DevOps, Cloud, Kubernetes, IaC, GitOps, MLOps 🔁 Consider a Repost if this is helpful