Free AI + Cloud Resources for Learning

🚀 Exciting Announcement – Azure Cloud Academy’s Next Training is Here! Over the past months, Azure Cloud Academy has been connecting with learners across 45+ countries 🌍. The energy, passion, and curiosity in our community keep pushing us to bring programs that are practical, future-focused, and industry-ready. That’s why we’re thrilled to launch our specialized program on Microsoft Azure AI Foundry Agents + AKS — designed to help professionals build, deploy, and scale real-world enterprise AI solutions. 💡 Course Highlights: 🔹 Azure AI Foundry Agents & Prompt Flow 🔹 Retrieval-Augmented Generation (RAG) with Vector Search 🔹 Containerization & CI/CD for LLM Apps 🔹 Deploying AI Agents on Azure Kubernetes Service (AKS) 🔹 Secure, Scalable, Enterprise-Grade AI Architectures 👨💻 Who should join? ✔ AI Engineers & Developers working with LLMs ✔ Cloud Professionals scaling GenAI on Kubernetes ✔ Solution Architects building production-grade AI systems ✔ Anyone passionate about Azure AI + AKS combo ✅ Prerequisites: 📌 Basic knowledge of Python & REST APIs 📌 Familiarity with Azure fundamentals 📌 Experience with Containers (Docker/Kubernetes) 📚 Learning Outcomes: ✔ Understand AKS networking, ingress controllers & multi-container patterns ✔ Containerize & deploy LLM-powered AI agents ✔ Implement secure, scalable RAG workflows ✔ Deliver end-to-end GenAI solutions with observability & governance ✨ Whether you’re a developer, data scientist, or architect — this program will help you move from prototype → production with confidence. 👉 Seats are limited! If you’d like to be part of this journey, drop a comment “I’m interested” or DM us for early access. A big thanks to my amazing team, Olfa Arfani, Payal P & Gopal Meena 🙏 — and to every learner who continues to support the Azure Cloud Academy mission of free, global cloud learning. 🚀 Let’s build the future of AI on Azure, together! #Azure #AI #LLM #AKS #FinOps #AzureCloudAcademy #GenAI #AzureAI #DevOps #Kubernetes

😍 G'day Beautiful People...today I am in New Zealand...specifically Auckland...running a Generative AI for Execs course...so I am super excited...as it's one of my favourite courses. Check out the video below to learn more 👇 I also wanted to introduce you all to a brand new feature called AWS Skills Profile...🎯 As some of you may know...I have achieved all 12 AWS certifications plus 2 instructor certs...so my profile for certifications looks rather full. But your's can look full too...🎉 👀In addition to outlining your certs it also showcases the Cloud Quests you have completed, Simulearns completed, labs completed and other cool Credly badges. 🙏It's a great way to show your breadth and depth of AWS experience. Take a peek at mine here 👇 https://lnkd.in/gmsY96mg Simply sign into AWS Skill Builder...go into Account, Profile...scroll down and Click on Skills Profile at the bottom 😎 If you're not familiar with Simulearn artifacts they are awesome hands-on game-based learning. With more hands-on and less game than the Cloud Quests. They are my fav. Check out all the Generative AI Simulearns (100+) here 👇 https://lnkd.in/gPB8K3qM That's it my friends...have an amazing week and stay safe. At Digital Colmer signing off...😍 P #TheDigitalCoach #AWS #GenerativeAI #AI #AgenticAI #Training #Certification #Auckland #Gaming #Learning #Skills

A few weeks ago, I stood in front of my class and asked my students a simple question: "What does it take to turn an idea on your laptop into a product the whole world can use?" Silence. Then one of them said softly, “Deploy it to the cloud?” I smiled. “Exactly. But there’s more to it than just deploying.” Over the past few weeks, we’ve been on an incredible journey together — exploring Docker to containerize apps, diving deep into LangChain and LangGraph to build powerful AI workflows, experimenting with RAG for smarter AI models, and leveraging OpenRouter and Google AI Studio for free, open-source APIs. On the cloud side, they learned how AWS ECS, ECR, and Fargate can take that local project and scale it securely across the globe. We even discussed the hidden guardians of the internet — VPCs and Load Balancers, which silently protect our data and manage traffic like skilled gatekeepers. Fast forward to today: My students are no longer just learners. They are builders. They are innovators. Some have already launched real web apps powered by the APIs we explored together. Here are just a few examples of their work: 🔹 Yam Yangfo – AI Suite 🔹 Kristina Gurung– AI-ppt-maker 🔹 Anusha Chettri-AI summarizer 🔹 Mamta Poudyal-AI summarizer. 🔹 Maya subba-AI summarizer. Link from AWS -ECS for accessing the application. AI Suite: https://lnkd.in/gzq8k4PK pptmaker:-https://lnkd.in/gzv-Tpaz AI-summarizer:-https://lnkd.in/g_Ssu9sS Github Link:-https://lnkd.in/gqNiCDNs Important notes on, how to setup ECS-ECR:-https://lnkd.in/gTDSRdub Use of Langchain:-https://lnkd.in/ge5MjRSB This isn’t just about coding. It’s about understanding how AI tools live and breathe inside a cloud ecosystem, and how containerization makes ideas portable and unstoppable. Here’s the reality: Once upon a time, a calculator was a separate, bulky device. Today, it’s hidden inside every phone, laptop, and even chatbot. AI is heading in the same direction. Soon, every product, every service, every experience will have AI woven into it. The choice we have is simple: Adapt and build with it now or be left behind as a spectator. I couldn’t be prouder of my students for choosing the first path. Check out their projects, and let us know in the comments how these ideas could impact your industry. The future isn’t coming. They’re building it. 🚀 #AI #CloudComputing #Docker #GenAI #OpenRouter #Innovation LangChain Amazon Web Services (AWS)Ollama

Practical MLOps: Operationalizing Machine Learning Models (Noah Gift and Alfredo Deza, September 2021) Overview Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models. Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start. ------- This book helps you: 📝 Apply DevOps best practices to machine learning 📝 Build production machine learning systems and maintain them 📝 Monitor, instrument, load-test, and operationalize machine learning systems 📝 Choose the correct MLOps tools for a given machine learning task 📝 Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware

If anyone is interested in developing their skills in Artificial Intelligence (AI), a quick thought based on my experience that might be helpful. 💬 Here are some tips for developing this skill: Dont fully rely on your college start your research online many platforms like coursera, google cloud, ibm, oracle they provide online courses for learning ai ml and deep learning Start your coding journey simple, pick one programming language that you are comfortable with dont mix up with multiple language Pick accordingly and start one by one

Back to Basics: Machine Learning on AWS Well this time my students wanted me to write something on Machine Learning and how AWS Service - Amazon Sagemaker AI can help Support the entire Machine Learning Lifecyle. Luckily i attended a Session on Machine Learning on AWS Couple of weeks back and made extensive notes. Please check out my notes below. Will be publishing more on these topics as i continue to explore and write notes. Follow me for more such content. I write & train people on real world insights related to AWS, Azure, GCP, DevOps, SRE, Platform Engineering, Migration/Modernization, Security, System Design, Solution Architecture, Artificial Intelligence, Machine Learning, Generative AI, Agentic AI, MLOps.

🚀 One of the most common questions I get: “Which AI/ML certification should I do?” In my network, I often see people sharing that they’ve completed courses from DeepLearning.ai, Coursera, and other platforms. While I haven’t done those myself (so I don’t have an opinion on them), I do hold all three AWS AI/ML certifications, and here’s what I think, based on my experience. 🔹 First, a key distinction: Unlike most AWS certifications that focus heavily on AWS services, the AI/ML track emphasizes machine learning concepts first, with AWS services layered on top. During my MBA, I took courses in Machine Learning and Statistics, and that foundation helped me a lot in these certifications. If you don’t have that foundation, I’d recommend starting with ML fundamentals (e.g., Andrew Ng, LinkedIn Learning, Udemy, etc.). 📚 Here’s a quick snapshot of each AWS AI/ML cert (in the order I suggest): 1️⃣ AWS Certified AI Practitioner (Foundational) A beginner-friendly certification that introduces you to AWS’s AI/ML services, concepts, and use cases — no heavy prerequisites. 2️⃣ AWS Certified Machine Learning Engineer – Associate Builds on foundational knowledge by covering the end-to-end ML workflow — data prep, model training, tuning, and deployment on AWS. 3️⃣ AWS Certified Machine Learning – Specialty The most advanced level, delving into deep learning, algorithmic optimization, and designing production-scale ML solutions on AWS. ✅ If your aim is to just get started with AWS AI/ML, go with the Practitioner first. From there, move to Associate and then Specialty as you gain more knowledge and hands-on experience. 💬 Which AI/ML certifications or courses have been most helpful in your journey? #AWS #AWSCertified #AWSCertification

💡 𝗛𝗼𝘄 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗔𝗜 𝗶𝗻 𝟮𝟬𝟮𝟱 (𝗡𝗼𝘁 𝗳𝗼𝗿 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀) Most people watch AI courses. The smart ones 𝗯𝘂𝗶𝗹𝗱, 𝗯𝗿𝗲𝗮𝗸, 𝗮𝗻𝗱 𝗿𝗲𝗯𝘂𝗶𝗹𝗱. If you’re past the basics and want to engineer AI, not just “learn it,” here’s your roadmap 👇 🚀 1️⃣ 𝗟𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗖𝗼𝘂𝗿𝘀𝗲𝗿𝗮 𝗰𝗼𝘂𝗿𝘀𝗲𝘀 Skip the intros — go advanced: 🧠 Deep Learning Specialization – Andrew Ng ⚙️ MLOps Specialization – DeepLearning.AI x Google Cloud 🤖 Generative AI with LLMs – AWS x DeepLearning.AI ☁️ ML Engineering for Production (MLOps) – Google 🔬 Advanced ML on GCP – Google Cloud 💡 Tip: After every concept, implement it immediately — notebook, container, or cloud lab. 💻 2️⃣ 𝗟𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝗚𝗶𝘁𝗛𝘂𝗯, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗖𝗼𝘂𝗿𝘀𝗲𝗿𝗮 Reverse-engineer real repos — that’s where mastery happens: 🧠 𝗟𝗟𝗠 𝗔𝗽𝗽𝘀: microsoft/autogen mckaywrigley/chatbot-ui langchain-ai/langchain ⚙️ 𝗠𝗟𝗢𝗽𝘀: kubeflow/kubeflow mlflow/mlflow zenml-io/zenml ☁️ 𝗖𝗹𝗼𝘂𝗱: aws-samples/amazon-sagemaker-examples GoogleCloudPlatform/mlops-on-gcp 🧩 𝗛𝗼𝘄 𝘁𝗼 𝗹𝗲𝗮𝗿𝗻 𝗳𝗮𝘀𝘁: 1️⃣ Fork → deploy → break it 2️⃣ Debug → fix → rebuild with your twist That’s where the real learning happens. ⚡ 𝗖𝗼𝘂𝗿𝘀𝗲𝗿𝗮 = 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 💻 𝗚𝗶𝘁𝗛𝘂𝗯 = 𝗰𝗵𝗮𝗼𝘀 𝗠𝗮𝘀𝘁𝗲𝗿 𝗯𝗼𝘁𝗵 → 𝘆𝗼𝘂 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗔𝗜. 🔥 𝗧𝗵𝗲 𝘁𝗿𝘂𝘁𝗵: AI isn’t learned. It’s 𝗯𝘂𝗶𝗹𝘁. #AI #MachineLearning #MLOps #Coursera #GitHub #DeepLearning #CareerGrowth

🚀 Free Machine Learning Learning Path on Azure 🌟 Want to master ML with Microsoft Azure for free? Azure offers a step-by-step ML journey with labs and free learning modules. Here’s your Azure ML Free Learning Path: 1️⃣ Start with Basics 🔗 Introduction to Machine Learning on Azure 📌 Understand AI, ML, and Azure ML Studio. 2️⃣ Hands-On with Azure ML Studio 🔗 Create No-Code ML Models in Azure 📌 Build models without coding, great for beginners. 3️⃣ Data Science & ML Advanced Modules 🔗 Azure Machine Learning Learning Path 📌 Explore training pipelines, MLOps, deployment, and monitoring. 4️⃣ AI School (Free Hub) 🔗 Microsoft AI School 📌 Curated AI/ML courses, hands-on labs, and challenges. ✨ Bonus: Try Azure ML Free Tier 👉 https://lnkd.in/gRAvM7cT ✅ By following this, you’ll be ready for real-world ML projects on Azure! #Azure #MachineLearning #MicrosoftAI #Upskilling

Thinking of moving from DevOps to MLOps? Here’s how to get started 👇 MLOps is not as scary as it sounds. If you know automation, cloud, and CI/CD, you’re already halfway there. Here’s what helps: • Learn the basics of machine learning—just enough to understand how models work. • See how MLOps adds a few new steps to DevOps: things like tracking models, retraining them, and keeping an eye out for changes. • Pick up some Python, and try out popular ML tools like TensorFlow or PyTorch (no need to be a data scientist!). • Get comfortable with tools like Kubeflow, MLflow, and model monitoring dashboards. • Build your own project: Try deploying a simple ML model, put it in a pipeline, and see what challenges pop up. • Most important: Don’t worry about being an expert from day one. Use your existing strengths, keep learning, and connect with the MLOps community. Your DevOps knowledge is super valuable in the world of AI. Upskill one step at a time and you’ll find new, exciting opportunities ahead! #DevOps #MLOps #AICareers #CareerGrowth #TechJobs