
What we’re about
Welcome to the DataOps Labs India. This is a community group of members join together and share Cloud, Data, AI, Generative AI, Machine Learning, DevOps and Security professionals where we will share experiences, Recent updates with use cases, technology challenges and trends.
Here all session were provided as knowledge sharing and their view only. Not opinion of company they associated.
Individual who interest in learn from industry experts on various topics include, Complex Application Architecture, Bigdata, AI/ML, MLOps, DevOps and Security with usecases.
RSVP rules:
1. Update your RSVP within 48 hours, if your plans change.
2. No Show. If you RSVP for an event and did not turn up without notifying us, you will be marked as No Show. A second No Show will result in removal from our group to give other members a fair chance of attending.
3. Waitlist. Please monitor your status right to the start time of the event. If you can’t check your status, you should remove yourself from the waiting list.
As a general rule, we encourage you to only RSVP YES to events that you can commit to attending. In the event of an emergency or illness, it is understandable that you won’t be able to attend.
Upcoming events (2)
See all- Building a Full-Stack Budget Tracker Application with Amazon Q CLILink visible for attendees
This is series of event - Amazon Q Developer
Description:
Join us for a hands-on, interactive session where we’ll explore how to build a production-ready Budget Tracker application using the Amazon Q CLI—AWS's AI-powered coding assistant.
In this meetup, we’ll walk through an iterative development process starting from scratch and evolving into a full-stack app. You’ll learn how to:- Use Behavior-Driven Development (BDD) for feature planning
- Apply Test-Driven Development (TDD) to validate functionality
- Auto-generate and refine code using Amazon Q CLI
- Build a REST API with Flask and a responsive UI
- Deploy using Docker, and test endpoints with Postman
📚 You’ll walk away with: ✅ A working budget tracker app
✅ A practical understanding of AI-assisted development
✅ Access to all prompts, code, and learning resources on GitHub
🎯 Who Should Attend:- Developers exploring GenAI tools like Amazon Q
- Cloud and full-stack enthusiasts
- Anyone curious about practical AI for coding
Blogs:
TDD vs BDD with Amazon Q Developer: Why Test-Driven Development Shines with Code Assistants - https://blog.dataopslabs.com/tdd-vs-bdd-with-amazon-q-developer-why-test-driven-development-shines-with-code-assistants
Building a Full-Stack Budget Tracker Application with Amazon Q CLI - https://blog.dataopslabs.com/building-a-full-stack-budget-tracker-application-with-amazon-q-cli-an-iterative-approach
Deploying a Flask Application to AWS Fargate with Amazon Q - https://blog.dataopslabs.com/deploying-a-flask-application-to-aws-fargate-with-amazon-q-a-journey-of-no-touch-codingGithub Code:
https://github.com/jayyanar/simple-budget-trackerStep by Step Prompt: https://github.com/jayyanar/simple-budget-tracker/blob/main/MYPROMPT.md
- Deploying a Flask Application to AWS Fargate with Amazon Q CLILink visible for attendees
This is series of event - Amazon Q Developer
Join us for a behind-the-scenes walkthrough of deploying a containerized Flask application to AWS Fargate using nothing but prompts—with Amazon Q CLI as your AI DevOps assistant.
In this hands-on session, we’ll deep dive into a real-world “no-touch coding” deployment, where every template, fix, and optimization was driven by conversational prompts, not manual code.
🔧 What you'll learn:- How to containerize and deploy a Flask app to AWS Fargate using Amazon Q
- Handling real-world issues like IAM policy errors, Docker execution bugs, and architecture mismatches
- Creating conditional SAM templates for reusable infrastructure
- Scaling Fargate services with CPU-based auto-scaling
- The power (and limitations) of “no-touch coding” with GenAI
🚀 Key Highlights:
- Full walkthrough of AWS SAM template generation via Amazon Q
- Dockerfile optimizations for ARM vs. x86 environments
- Deployment strategies using ECR, ECS, and Load Balancers
- Error resolution using iterative prompts to Amazon Q—no manual coding!
👨💻 Who Should Attend:
- DevOps engineers and cloud developers working with AWS
- Developers curious about AI-assisted infrastructure deployment
- Anyone exploring Amazon Q CLI for real-world use cases
📦 Bonus: Access to the full GitHub repo, blog walkthrough, and ready-to-use deployment templates.
Blogs:
TDD vs BDD with Amazon Q Developer: Why Test-Driven Development Shines with Code Assistants - https://blog.dataopslabs.com/tdd-vs-bdd-with-amazon-q-developer-why-test-driven-development-shines-with-code-assistants
Building a Full-Stack Budget Tracker Application with Amazon Q CLI - https://blog.dataopslabs.com/building-a-full-stack-budget-tracker-application-with-amazon-q-cli-an-iterative-approach
Deploying a Flask Application to AWS Fargate with Amazon Q - https://blog.dataopslabs.com/deploying-a-flask-application-to-aws-fargate-with-amazon-q-a-journey-of-no-touch-codingGithub Code:
https://github.com/jayyanar/simple-budget-trackerStep by Step Prompt: https://github.com/jayyanar/simple-budget-tracker/blob/main/MYPROMPT.md