This is how you turn ML models into real, scalable systems. MadeWithML is the best place to learn how to combine AI/ML with software engineering to build production-grade solutions. These fundamentals are directly applicable to LLMOps as well. 100% free and open-source.

Have you tried @MadeWithML ?

This is how you turn ML models into real, scalable systems. MadeWithML is the best place to learn how to combine AI/ML with software engineering to build production-grade solutions. These fundamentals are directly applicable to LLMOps as well. 100% free and open-source.

First Principles Approach to MLOps! MadeWithML is the best place to learn how to combine AI/ML with software engineering to build production-grade solutions. These fundamentals are directly applicable to LLMOps as well. 100% free and open-source.

🚀 Learn Tech Skills for FREE in 2025/2026: ☑️ Python ➟ learnpython. org ☑️ Data Science ➟ coursera. org (audit free) ☑️ MLOps ➟ madewithml. com ☑️ Machine Learning ➟ kaggle. com/learn ☑️ SQL ➟ w3schools. com/sql ☑️ AWS Cloud ➟ skillbuilder. aws ☑️ Docker ➟ learndocker. online ☑️ DevOps Basics ➟ devopscommunity. in ☑️ HTML ➟ html. com ☑️ CSS ➟ web. dev/learn/css ☑️ JavaScript ➟ javascript. info ☑️ React ➟ reactplay. io ☑️ API Development ➟ rapidapi. com/learn ☑️ Git & Version Control ➟ git-scm. com/book ☑️ AI Fundamentals ➟ elementsofai. com ☑️ Web3 ➟ learnweb3. io ☑️ Blockchain ➟ cryptozombies. io ☑️ eBPF / Observability ➟ ebpf. io ☑️ Vue.js ➟ learnvue. co ☑️ Next.js ➟ nextjs. org/learn ☑️ Go ➟ learn-golang. org ☑️ Rust ➟ rust-lang. org/learn ☑️ PHP Best Practices ➟ phptherightway. com ☑️ Design Patterns ➟ refactoring. guru ☑️ Service Mesh / Cloud Native Infra ➟ solo. io/solo-academy ☑️ Cloud Native Ecosystem ➟ cncf. io ☑️ Open Source Community ➟ eddiehub. org 💡Focus on labs & basics for quick wins. 💡Learn ➟ Build ➟ Grow ➟ Repeat Everything above is free - no excuses to start today. Pro tip: Pair No-Code with AI via tools like Bubble OpenAI for rapid prototypes. 📌 I daily share: ~ AI & ML Insights ~ Cloud Strategies ~ No-Code Hacks ~ DevOps Tools ~ Security Trends ~ Tech Roadmaps & Resources Follow me (@techyoutbe ) for more such amazing threads.

MadeWithML 这个仓库不错，对于想做生产级别的 ML 应用应该有帮助。 github.com/GokuMohandas/Made…

First Principles Approach to MLOps! MadeWithML is the best place to learn how to combine AI/ML with software engineering to build production-grade solutions. These fundamentals are directly applicable to LLMOps as well. 100% free and open-source.

𝗠𝗟 & 𝗠𝗟𝗢𝗽𝘀 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻* → Machine Learning Specialization – DeepLearning.AI 🔗 lnkd.in/dgVRyNds → Designing Machine Learning Systems (Book) 🔗 lnkd.in/da35r__z → Model deployment in production 🔗 lnkd.in/d8ueZQ2A 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 (𝗣𝘆𝘁𝗵𝗼𝗻)* → Python general 🔗 lnkd.in/dEZHAAKZ → FastAPI / Flask 🔗 lnkd.in/dpADd3gh → Git (Version Control) 🔗 lnkd.in/dytQZZm2 → Unit Testing 🔗 lnkd.in/d-sYNxxz → Integration Testing 🔗 lnkd.in/d6waJuGp → Docker (Must) 🔗 lnkd.in/dnKai3jV → CI/CD (only one) 🔗 GitHub Actions → lnkd.in/dWTbrCZK 🔗 CircleCI → lnkd.in/dtJBXEAP 🔗 Jenkins → lnkd.in/didiekvW → Load Testing (Locust) 🔗 lnkd.in/dJiWpD-z → A/B Testing 🔗 lnkd.in/dbPHAuWH 𝗖𝗹𝗼𝘂𝗱 𝗜𝗻𝗳𝗿𝗮 (𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁)* Pick one cloud provider. Learn via certification paths. → AWS SageMaker (overview) 🔗 lnkd.in/dhpHEfNj AWS Learning Path: 1️⃣ AWS Cloud Practitioner (free) → lnkd.in/dVZUbXFZ 2️⃣ AWS ML Associate → lnkd.in/dcHn3eH4 3️⃣ AWS ML Specialty → lnkd.in/drRg8Hkt (Also: GCP Vertex AI, Azure ML) 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁 𝗧𝗿𝗮𝗰𝗸𝗶𝗻𝗴 & 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 → MLflow* 🔗 lnkd.in/dPrF_8h2 → Grafana Prometheus 🔗 lnkd.in/dkvQY2Uv → DataDog 🔗 lnkd.in/dKvkcN48 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗦𝗲𝗿𝘃𝗶𝗻𝗴 → Apache Airflow (good to know) 🔗 lnkd.in/d-SmTBYA → Kubeflow (best with GCP) → MetaFlow → EC2 / ECS / Kubernetes / Step Functions 👉 Skip Kubernetes early on unless required 🚨𝗥𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗲𝗱 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 → MLOps Zoomcamp (DataTalksClub) 🔗 lnkd.in/dNUAY_iw → MadeWithML by Goku 🔗 lnkd.in/dRMprs-C → Train & Deploy ML (GitHub) 🔗 lnkd.in/dcyKaTp5 → Taking Python to Production 🔗 lnkd.in/dJ7a7tTH credits to @ShantanuLadhwe for the roadmap and providing the links and the post.

First Principles Approach to MLOps! MadeWithML is the best place to learn how to combine AI/ML with software engineering to build production-grade solutions. These fundamentals are directly applicable to LLMOps as well. 100% free and open-source.

If you know MLOps you are already ahead of 99% of the market. It requires working on various skills. I really like this roadmap, it has solid suggestions to start learning, today! ▶️ 𝗠𝗟 & 𝗠𝗟𝗢𝗽𝘀 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻* → Machine Learning Specialization – DeepLearning.AI - 🔗   lnkd.in/dgVRyNds → Designing Machine Learning Systems (Book) - 🔗 lnkd.in/da35r__z → Model deployment in production - 🔗 lnkd.in/d8ueZQ2A ▶️ 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 (𝗣𝘆𝘁𝗵𝗼𝗻)* → Python general - 🔗 lnkd.in/dEZHAAKZ → FastAPI / Flask - 🔗 lnkd.in/dpADd3gh → Git (Version Control) - 🔗 lnkd.in/dytQZZm2 → Unit Testing - 🔗 lnkd.in/d-sYNxxz → Integration Testing - 🔗 lnkd.in/d6waJuGp → Docker (Must) - 🔗 lnkd.in/dnKai3jV → CI/CD (only one) 🔗 GitHub Actions → lnkd.in/dWTbrCZK 🔗 CircleCI → lnkd.in/dtJBXEAP 🔗 Jenkins → lnkd.in/didiekvW → Load Testing (Locust) - 🔗 lnkd.in/dJiWpD-z → A/B Testing - 🔗 lnkd.in/dbPHAuWH ▶️ 𝗖𝗹𝗼𝘂𝗱 𝗜𝗻𝗳𝗿𝗮 (𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁)* Pick one cloud provider. Learn via certification paths. → AWS SageMaker (overview) - 🔗 lnkd.in/dhpHEfNj → AWS Learning Path: 1️⃣ AWS Cloud Practitioner (free) → lnkd.in/dVZUbXFZ 2️⃣ AWS ML Associate → lnkd.in/dcHn3eH4 3️⃣ AWS ML Specialty → lnkd.in/drRg8Hkt (Also: GCP Vertex AI, Azure ML) ▶️ 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁 𝗧𝗿𝗮𝗰𝗸𝗶𝗻𝗴 & 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 → MLflow* - 🔗 lnkd.in/dPrF_8h2 → Grafana Prometheus - 🔗 lnkd.in/dkvQY2Uv → DataDog - 🔗 lnkd.in/dKvkcN48 ▶️ 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗦𝗲𝗿𝘃𝗶𝗻𝗴 → Apache Airflow (good to know) - 🔗 lnkd.in/d-SmTBYA → Kubeflow (best with GCP) → MetaFlow → EC2 / ECS / Kubernetes / Step Functions (Skip Kubernetes early on if possible) 🚨𝗥𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗲𝗱 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 → MLOps Zoomcamp (DataTalksClub) 🔗 lnkd.in/dNUAY_iw → MadeWithML by Goku 🔗 lnkd.in/dRMprs-C → Train & Deploy ML (GitHub) 🔗 lnkd.in/dcyKaTp5 → Taking Python to Production by 🎧 Eric (Udemy) 🔗 lnkd.in/dJ7a7tTH Credits to Shantanu Ladhwe for his solid roadmaps.

MLOps roadmap from a senior with 6 years of experience (with link to resources and courses): > 𝗠𝗟 & 𝗠𝗟𝗢𝗽𝘀 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻* → Machine Learning Specialization – DeepLearning.AI - 🔗   lnkd.in/dgVRyNds → Designing Machine Learning Systems (Book) - 🔗 lnkd.in/da35r__z → Model deployment in production - 🔗 lnkd.in/d8ueZQ2A > 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 (𝗣𝘆𝘁𝗵𝗼𝗻)* → Python general - 🔗 lnkd.in/dEZHAAKZ → FastAPI / Flask - 🔗 lnkd.in/dpADd3gh → Git (Version Control) - 🔗 lnkd.in/dytQZZm2 → Unit Testing - 🔗 lnkd.in/d-sYNxxz → Integration Testing - 🔗 lnkd.in/d6waJuGp → Docker (Must) - 🔗 lnkd.in/dnKai3jV → CI/CD (only one) 🔗 GitHub Actions → lnkd.in/dWTbrCZK 🔗 CircleCI → lnkd.in/dtJBXEAP 🔗 Jenkins → lnkd.in/didiekvW → Load Testing (Locust) - 🔗 lnkd.in/dJiWpD-z → A/B Testing - 🔗 lnkd.in/dbPHAuWH > 𝗖𝗹𝗼𝘂𝗱 𝗜𝗻𝗳𝗿𝗮 (𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁)* Pick one cloud provider. Learn via certification paths. → AWS SageMaker (overview) - 🔗 lnkd.in/dhpHEfNj → AWS Learning Path: 1. AWS Cloud Practitioner (free) → lnkd.in/dVZUbXFZ 2. AWS ML Associate → lnkd.in/dcHn3eH4 3. AWS ML Specialty → lnkd.in/drRg8Hkt (Also: GCP Vertex AI, Azure ML) > 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁 𝗧𝗿𝗮𝗰𝗸𝗶𝗻𝗴 & 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 → MLflow* - 🔗 lnkd.in/dPrF_8h2 → Grafana Prometheus - 🔗 lnkd.in/dkvQY2Uv → DataDog - 🔗 lnkd.in/dKvkcN48 > 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗦𝗲𝗿𝘃𝗶𝗻𝗴 → Apache Airflow (good to know) - 🔗 lnkd.in/d-SmTBYA → Kubeflow (best with GCP) → MetaFlow → EC2 / ECS / Kubernetes / Step Functions (Skip Kubernetes early on if possible) 🚨𝗥𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗲𝗱 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 → MLOps Zoomcamp (DataTalksClub) 🔗 lnkd.in/dNUAY_iw → MadeWithML by Goku 🔗 lnkd.in/dRMprs-C → Train & Deploy ML (GitHub) 🔗 lnkd.in/dcyKaTp5 → Taking Python to Production by 🎧 Eric (Udemy) 🔗 lnkd.in/dJ7a7tTH red * means must-have skills Credit to Shantanu Ladhwe for his amazing roadmaps.

haa dekha unka, achha hai

thanks for the mention! here are a few resources • applied-llms.org • applyingml.com • eugeneyan.com/start-here/

can search for the same from @eugeneyan and chip huyen

@MadeWithML is the best one out there!

Madewithml is 🔥!!

These are some top repos! Big fan of @MadeWithML

4️⃣ Made with ML by @MadeWithML The course teaches how to build an end-to-end ML system, including tracking, testing, serving, orchestration, and monitoring. You can join a live cohort for $150 or take the course in a self-paced mode for free: madewithml.com/courses/mlops… 5/6

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Apprenez des compétences GRATUITEMENT : Python ➟ learnpython .org Data Science ➟ coursera .org MLOps ➟ madewithml .com Machine Learning ➟ kaggle .com SQL ➟ w3schools .com/sql AW S ➟ skillbuilder .aws Docker ➟ learndocker .online DevOps ➟ devopsguys .in