Deploy Code Quickly on AWS

DevOps Case Study: Reducing Deployment Time by 80% for a Healthcare Platform https://lnkd.in/gTEwnr5G 𝐁𝐚𝐜𝐤𝐠𝐫𝐨𝐮𝐧𝐝: A healthcare client was facing long release cycles — deploying new features took 4–5 hours, involving manual testing, approvals, and coordination between multiple teams. Frequent hotfixes often led to downtime, frustrating both developers and end users. 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬: Manual deployments prone to human error Inconsistent environments (dev/stage/prod) Slow feedback loop between development and operations Limited observability into failures 𝐃𝐞𝐯𝐎𝐩𝐬 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐞𝐝: ✅ CI/CD Pipeline: Used Jenkins + GitHub Actions to automate build, test, and deployment pipelines. ✅ Infrastructure as Code (IaC): Provisioned environments using Terraform and Ansible, ensuring consistent configuration across AWS EC2 instances. ✅ Containerization: Migrated applications to Docker containers and orchestrated them via Kubernetes to improve scalability and rollbacks. ✅ Monitoring & Alerts: Integrated Prometheus + Grafana dashboards and Slack alerts for real-time observability. ✅ Security Integration: Added Snyk for vulnerability scanning and HashiCorp Vault for secrets management. 𝐑𝐞𝐬𝐮𝐥𝐭𝐬: Deployment time reduced from 4 hours to 25 minutes Rollback time dropped from 30 minutes to under 5 minutes Deployment frequency increased by 5x Teams gained confidence to release more often, with fewer incidents 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲: DevOps is not just automation — it’s about building a culture of collaboration, continuous improvement, and accountability across teams. Watch the DevOps projects - https://lnkd.in/gTEwnr5G Connect with me on Instagram - https://lnkd.in/gYG3QNfh Read this post till here? Do liek and share with your community #DevOps #CaseStudy #CICD #Automation #Kubernetes #Cloud #Terraform #Ansible #Jenkins #EngineeringExcellence

I can't believe its almost 5 years since I started developing 𝒆𝒛𝒔𝒎𝒅𝒆𝒑𝒍𝒐𝒚 for simplifying deployment of models to AWS AI services like SageMaker (see original blogs below), and now Amazon Bedrock. Today you can deploy SOTA models like 𝐃𝐞𝐞𝐩𝐬𝐞𝐞𝐤 on Amazon SageMaker and Amazon Bedrock (through Custom Model Import) with a 𝘴𝘪𝘯𝘨𝘭𝘦 𝘭𝘪𝘯𝘦 𝘈𝘗𝘐. Whether its a custom model you have locally, on S3, or on a hub like #huggingface - try this out to easily deploy and test out your AI APIs. ✍ This reduces boilerplate code significantly (99.5%) if you go by lines of code needed to do fairly sophisticated things like custom containers, autoscaling, serverless endpoints, model monitoring, and deploying the same models to Bedrock and SageMaker where applicable 📈 The SDK has been downloaded 100s of 1000s of times across various regions, and has helped speed up PoCs/development significantly for major customers in every vertical! Some contributions are even from customers of AWS in these verticals. When you have your AI endpoint/API deployed successfully, you can now integrate this with popular tools like Langchain and Llamaindex. - With LangChain https://lnkd.in/ewHijdr3 - With LlamaIndex https://lnkd.in/egKqJC7F - With CrewAI https://lnkd.in/eaR4BPTV - thanks for your PR Bobby Lindsey ;) - And more! Past blogs and writeups: - Deploy machine learning models to Amazon SageMaker using the ezsmdeploy Python package and a few lines of code - https://lnkd.in/eiDqnBeC - Deploy Large Language Models Easily with the New ezsmdeploy Python SDK https://lnkd.in/e3UbYhub Made with ❤️ for the open source community. Do try it out, contributions are welcome! Get started with 𝘱𝘪𝘱 𝘪𝘯𝘴𝘵𝘢𝘭𝘭 𝘦𝘻𝘴𝘮𝘥𝘦𝘱𝘭𝘰𝘺

Leveraging the power and ease of the AWS Serverless Application Model (SAM) to quickly deploy a real-time weather dashboard application. 🚀 🔍 Project Overview: The application provides current weather information and a 5-day forecast based on the user’s latitude and longitude. It fetches data from the OpenWeatherMap API and displays it in a user-friendly format. 🔧 Key Features: • Real-time weather data including temperature in both Celsius and Fahrenheit, weather description, humidity, and wind speed. • 5-day weather forecast. • Displays the city name based on the provided coordinates. 💡 Why AWS SAM? AWS SAM made it incredibly easy to build, package, and deploy this serverless application in a very short amount of time. Here’s a quick rundown of the process: 1. Update the Lambda Function: Write a simple Python script to fetch weather data. 2. Store API Key Securely: Use AWS Systems Manager Parameter Store to securely store and retrieve the OpenWeatherMap API key. 3. Build and Package: Use SAM CLI commands to build and package the application. 4. Deploy: Deploy the entire stack with a single command, thanks to SAM’s integration with AWS CloudFormation. 5. Host Static Website on S3: Use S3 to host the static front-end, making the application accessible to users. 🚀 Speed and Efficiency with SAM: AWS SAM significantly reduces the time and effort needed to deploy serverless applications. With SAM, you can define all the necessary resources in a simple YAML template and manage your entire application as a single unit. This eliminates the need for manually configuring individual services, allowing you to focus more on writing code and delivering value. 🔗 Technology Stack: • AWS Lambda: Fetches weather data from the OpenWeatherMap API. • Amazon API Gateway: Serves as the HTTP endpoint for the Lambda function. • AWS Systems Manager Parameter Store: Securely stores and retrieves the API key. • Amazon S3: Hosts the static website. • Amazon CloudFront (optional): Serves the website over HTTPS for secure access to the Geolocation API. This simple project is a testament to how AWS SAM simplifies the deployment of serverless applications, allowing developers to focus more on writing code and less on infrastructure management. Check out the project on GitHub: https://lnkd.in/dkWRBhQj I would love to mention some amazing individuals who have inspired me and who I learn from and collaborate with: Neal K. Davis Steven Moran A Sohail Eric Huerta Prasad Rao Azeez Salu Maria Christidi Noble Kent Burgdorfer Stéphane Maarek 🔔 Follow me for more updates on AWS, serverless technologies, and real-world projects. #AWS #Serverless #AWSSAM #CloudComputing #WeatherDashboard #Tech #Programming #Python #APIGateway #S3 #CloudFront #ParameterStore

𝗙𝗿𝗼𝗺 𝗟𝗼𝗻𝗴 𝗡𝗶𝗴𝗵𝘁𝘀 𝗼𝗳 𝗠𝗮𝗻𝘂𝗮𝗹 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀 𝘁𝗼 𝗢𝗻𝗲-𝗖𝗹𝗶𝗰𝗸 𝗖𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲: 𝗧𝗵𝗲 𝗖𝗜/𝗖𝗗 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 In the early days, 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀 𝘄𝗲𝗿𝗲 𝗮 𝗺𝗶𝘅 𝗼𝗳 𝘀𝘁𝗿𝗲𝘀𝘀, 𝗰𝗮𝗳𝗳𝗲𝗶𝗻𝗲, 𝗮𝗻𝗱 𝘂𝗻𝗰𝗲𝗿𝘁𝗮𝗶𝗻𝘁𝘆. We manually packaged builds, transferred them to servers, updated configurations, and ran smoke tests while praying everything stayed up. One missing dependency or mismatched environment variable could send production spiraling. That all changed the day we started building proper 𝗖𝗜/𝗖𝗗 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀. Using 𝗝𝗲𝗻𝗸𝗶𝗻𝘀 for build automation and 𝗚𝗶𝘁𝗛𝘂𝗯 𝗔𝗰𝘁𝗶𝗼𝗻𝘀 for version control triggers, we began integrating every code commit with 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁. What used to be a long, fragile release process turned into a 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗮𝗯𝗹𝗲, 𝗿𝗲𝗽𝗲𝗮𝘁𝗮𝗯𝗹𝗲 𝗳𝗹𝗼𝘄. When we containerized our apps with 𝗗𝗼𝗰𝗸𝗲𝗿, orchestrated them using 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 and 𝗱𝗲𝗽𝗹𝗼𝘆𝗲𝗱 𝘃𝗶𝗮 𝗔𝗪𝗦 𝗘𝗖𝗦 𝗮𝗻𝗱 𝗖𝗼𝗱𝗲𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲, 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 𝗯𝗲𝗰𝗮𝗺𝗲 𝘀𝗲𝗰𝗼𝗻𝗱 𝗻𝗮𝘁𝘂𝗿𝗲. The same 𝗦𝗽𝗿𝗶𝗻𝗴 𝗕𝗼𝗼𝘁 𝘀𝗲𝗿𝘃𝗶𝗰𝗲 that ran locally was what went live in production. No more environment mismatch, no manual patching. 𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 𝘄𝗲𝗿𝗲 𝗰𝗹𝗲𝗮𝗿:  • Internal dashboards that once needed weekend deployments could now go live with a 𝘀𝗶𝗻𝗴𝗹𝗲 𝗰𝗼𝗺𝗺𝗶𝘁 and 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗮𝗽𝗽𝗿𝗼𝘃𝗮𝗹.  • 𝗥𝗲𝗮𝗰𝘁 𝗳𝗿𝗼𝗻𝘁𝗲𝗻𝗱𝘀 bundled via Node pipelines were deployed to 𝗦𝟯 + 𝗖𝗹𝗼𝘂𝗱𝗙𝗿𝗼𝗻𝘁 automatically after a merge, with rollback handled through 𝘃𝗲𝗿𝘀𝗶𝗼𝗻𝗲𝗱 𝗮𝗿𝘁𝗶𝗳𝗮𝗰𝘁𝘀.  • 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗺𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 pushed through Jenkins pipelines went from 𝟮-𝗵𝗼𝘂𝗿 𝗺𝗮𝗻𝘂𝗮𝗹 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝘀 𝘁𝗼 𝘂𝗻𝗱𝗲𝗿 𝟭𝟬 𝗺𝗶𝗻𝘂𝘁𝗲𝘀 tested, built, and deployed automatically. But what CI/CD really changed wasn’t just speed , it changed ownership. Developers stopped dreading deployment day and 𝘀𝘁𝗮𝗿𝘁𝗲𝗱 𝗳𝗼𝗰𝘂𝘀𝗶𝗻𝗴 𝗼𝗻 𝘄𝗿𝗶𝘁𝗶𝗻𝗴 𝗰𝗹𝗲𝗮𝗻𝗲𝗿, 𝘁𝗲𝘀𝘁𝗮𝗯𝗹𝗲, 𝗮𝗻𝗱 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗰𝗼𝗱𝗲. Today, 𝗖𝗜/𝗖𝗗 𝗶𝘀 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝗮 𝗽𝗿𝗼𝗰𝗲𝘀𝘀. It’s the culture that allows modern teams to move fast, deliver confidently, and recover instantly. After 12 years in full-stack development, I can say this confidently: 𝗖𝗜/𝗖𝗗 𝗱𝗶𝗱𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁, 𝗶𝘁 𝗯𝘂𝗶𝗹𝘁 𝘁𝗿𝘂𝘀𝘁, 𝘀𝗽𝗲𝗲𝗱, 𝗮𝗻𝗱 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 𝗶𝗻𝘁𝗼 𝗲𝘃𝗲𝗿𝘆 𝗹𝗶𝗻𝗲 𝗼𝗳 𝗰𝗼𝗱𝗲 𝘄𝗲 𝗱𝗲𝗹𝗶𝘃𝗲𝗿.

DevOps Project for Your CV: Deploy Python App (Docker) to AWS ECS with GitHub Actions Here's the step-by-step plan to build and deploy your project 👇 1. Create a simple Python application Build a basic Python app (Flask or FastAPI) with a REST endpoint. 2. Dockerize your Python app Create a Dockerfile to containerize the app. 3. Create an Amazon ECR Repository Host your Docker image in Amazon’s Elastic Container Registry. 4. Set Up GitHub Actions Workflow Create .github/workflows/deploy.yml to automate image build & push. 5. Configure GitHub Secrets Add these secrets in your GitHub repo: - AWS_ACCESS_KEY_ID - AWS_SECRET_ACCESS_KEY - AWS_REGION - ECR_REPOSITORY - ECR_REGISTRY 6. Authenticate to AWS ECR from GitHub Actions In your workflow, use aws-actions/configure-aws-credentials and aws-actions/amazon-ecr-login to log in securely. 7. Push Docker Image to ECR GitHub Actions will: - Build your Docker image - Tag it - Push it to ECR on every push to main 8. Create an ECS Cluster (Fargate) Use AWS Console or CLI to set up your cluster. 9. Define a Task Definition Configure how your container runs, including image URL from ECR. 10. Create a Service Deploy the task and ensure it stays running. 11. Attach an Application Load Balancer Expose your app to the internet with an ALB. 12. Set IAM Roles and Permissions Ensure ECS, ECR, and GitHub Actions can communicate securely. 13. Access Your App via Public DNS Grab the ALB DNS to test your app. This project combines ECS, ECR, IAM, CI/CD & GitHub Actions, showcasing modern DevOps practices for your resume! Start building today!

🚀 Microservices CI/CD with AWS + Terraform – Step-by-Step Implementation I recently explored a complete end-to-end CI/CD pipeline setup for Microservices using AWS and Terraform — and it’s a game-changer for scalable, automated deployments! Here’s what the project covers: 🔹 Microservices Architecture – Breaking down monoliths into lightweight, independent services (Node.js, Python, Go). 🔹 CI/CD Workflow – Automated builds, tests, and deployments using AWS CodePipeline, CodeCommit, CodeBuild, and ECS. 🔹 Infrastructure as Code (IaC) – Full environment provisioning via Terraform, including IAM roles, S3 backends, and ECS resources. 🔹 Containerization – Dockerized applications deployed through ECS clusters for zero-downtime updates. 🔹 End-to-End Automation – Continuous integration, continuous delivery, and continuous deployment, all managed via code. 🧩 The setup shows how to: Build Docker images automatically from commits Deploy microservices to AWS ECS clusters Manage infrastructure and pipelines using reusable Terraform modules 💡 This integration highlights how DevOps + Cloud + IaC can work together to enable fast, reliable, and repeatable software delivery — the essence of modern engineering. If you’re interested in the Terraform + AWS CI/CD integration demo, the guide includes full Terraform scripts and AWS configurations for hands-on learning. #DevOps #AWS #Terraform #CICD #Microservices #InfrastructureAsCode #Automation #CloudEngineering