Apache Beam on Google Cloud Dataflow

Just finished building an AWS Resource Health Monitor in Python! This automation bot: - Monitors EC2 instances for idle CPU and missing tags - Stores timestamped health reports in S3 - Auto-remediates issues with least privilege IAM permissions - Schedules itself to run daily with EventBridge Built with Python, boto3, and real AWS infrastructure from scratch. Check it out on GitHub 👇:  https://lnkd.in/eKmp7gWd #AWS #Python #CloudComputing #DevOps #boto3 #CloudPractitioner

I used to click around AWS Console like I was playing a video game 🎮 Click here. Wait. Click there. Pray nothing breaks. Then I learned you can do ALL of that with just Python. I've been learning how to manage EC2 instances via Python (boto3) instead of the console. And honestly? No going back. Here's why Python > Console for EC2: 🔁 Repeatable: run the same script 100 times, same result. Console? Depends on your mood that day. ⚡ Faster: one script to launch, configure, and terminate. No tab switching, no clicking through 10 menus. 🧠 Version-controlled: your infra lives in code. Your team can read it, review it and reuse it. 🤖 Automatable: connect it to your ML pipeline. Spin up EC2 when needed, kill it when done. No bill surprises. The console is great for learning what exists. Python is how you actually work. Still clicking manually? Try boto3. Your future self will thank you. 😄 #AWS #Python #MachineLearning #MLOps #CloudComputing #LearningInPublic #boto3

Developers Deploy Python MCP Servers on Azure Kubernetes Service for Scalable AI Orchestration 📌 Developers now run Python MCP servers on Azure Kubernetes Service, letting AI agents autonomously manage clusters without human oversight. This opens the door to cross-cloud AI orchestration, decoupling tools like Google’s Gemini CLI from vendor lock-in. With auto-scaling, secure auth, and Python 3.13 support, it’s a game-changer for scalable, zero-trust AI infrastructure. 🔗 Read more: https://lnkd.in/dBX8Az3G #Python #Mcp #Azureaks #Aiorchestration #Googlegemini

🛠️ I published a new article about a task that seemed simple on paper but turned out to be much more complicated in practice: deploying a Streamlit app on Azure App Service. The app worked locally, the Azure setup seemed reasonable and a tech community blog I found seems helpful. Yet, in a private Azure ML environment, several details were different enough to break the whole process. So, I documented the part that usually gets overlooked: what failed, why it failed, and which configuration changes made the deployment work. This is not a generic "hello world" walkthrough, but rather a more in-depth explanation. It's a practical account of running a Python app in a realistic enterprise setup. The link to the article in comments 👇🏻 #Azure #Streamlit #Python #AzureAppService #AzureML #MLOps

My multi-agent A2A agent demo/article was accepted on the Google Community channel on Medium!! This paper covers a complete solution that uses the ADK, A2A, Python Backend, and a complete multi-agent architecture. The entire agent system was deployed to a GKE cluster. The Google Cloud Community Channel on Medium is here: https://lnkd.in/e5EvcQzU My article/demo is here: https://lnkd.in/eFP8VZaJ #GDE #ADK #A2A #Python #GKE #GoogleCloud

Week 9: Python fundamentals. Started with print("Hello"). Ended with a Python script deploying infrastructure to AWS. Everything in between - strings, lists, dictionaries, loops, functions - had an AWS example attached to it. That's the difference between learning Python and learning Python for cloud engineering. Full breakdown on Medium - link in the comments. #AWS #Python #LearningInPublic #CloudEngineering

How many unused Lambda functions are running in your AWS account right now? I built a simple Python script that helped identify 53 wasteful functions within minutes,highlighting gaps in visibility and cost optimization in serverless setups. Take a look: https://lnkd.in/d9evji-B

𝐀𝐖𝐒 𝐋𝐚𝐦𝐛𝐝𝐚 𝐟𝐮𝐧𝐜𝐭𝐢𝐨𝐧 Creating a Lambda function on AWS helps you save alot of time in building the server side components, configurations and setup. It can be used for various different purposes like stream processing, file processing, mobile apps, etc. You can choose the programming language that best fits you, and select the run time required to run the script. Easy to set up, some good add ons, like the layers feature is a good to know to reuse packages for newly created lambda functions rather than reuploading the modules each time you create a new function. To test run a python script that converts your password into hash and becomes the event that triggers the lambda function, you can follow the steps in my github repo. Github repo link: https://lnkd.in/d7QQwjnA #AWS #CLOUD #PYTHON #AI #CNAI #CLOUDPRACTITIONER #AWSLAMBDA

🚀 Day 1 of 60: Transitioning from WebDev to MLOps Engineer I’ve officially started my 60-day journey to transition into MLOps — and today marks Day 1. 🔧 What I built today: A Python script (with help from Anthropic’s Claude) to simulate a basic data pipeline The script generates test data locally and syncs it to Amazon Web Services S3 using the Boto3 SDK Set up and tested everything using the AWS CLI Enabled versioning on S3 to track changes and roll back data when needed 💡 This is a small but important step toward understanding how data pipelines and storage work in real-world MLOps systems. 📂 Code: https://lnkd.in/dVjCiCbv 🎥 Demo Video: https://lnkd.in/dikGTVMf

A stylized SVG illustration that visualizes the full project in a dark, technical aesthetic. It shows the three-stage pipeline on the left (CSV → Python → LDIF), the AWS VPC with the EC2 server and directory tree on the right, the CloudShell terminal with real command syntax, and key metrics summarized at the bottom. ❤️