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dsrathore1/README.md

πŸ‘‹ Hey there, I'm Digpal Singh Rathore!

Typing SVG


πŸš€ About Me

🎯 Mission: To automate, optimize, and innovate in the Cloud & AI domain

πŸ’‘ Vision: To revolutionize Cloud & AI integration and make MLOps more scalable, efficient, and accessible

πŸ”₯ Passion: Cloud DevOps Engineer & AI/MLOps Enthusiast with a deep love for automation, scalability, and intelligent cloud solutions.
I thrive on building resilient cloud architectures, optimizing CI/CD pipelines, and integrating AI-driven automation in DevOps workflows.

πŸ›  Strengths: Cloud-Native Development, CI/CD Pipelines, Observability, Microservices, Generative AI, Edge Computing

πŸ“š Learning: Advanced Kubernetes, Serverless Computing, AI for DevOps, Security Automation

πŸ“’ Motto: "Building the future, one automated pipeline at a time."


πŸš€ Currently Expanding My Skills in:

βœ… Advanced Terraform, Kubernetes & Serverless Architectures
βœ… Machine Learning Engineering & MLOps Best Practices
βœ… Cloud Security & AI-Driven DevOps Automation


🎯 Future Plans:

  • πŸ— Develop a cutting-edge MLOps pipeline integrating AI for self-healing infrastructure
  • πŸ” Master Cloud Security & FinOps to optimize cloud costs at scale
  • πŸ€– Build AI-powered DevOps automation tools for intelligent deployments
  • 🎯 Contribute to open-source projects in MLOps, Cloud Automation, and AI-driven DevOps

πŸ› οΈ Tools & Technologies I Use Daily

Here are some of the tools and technologies I work with:

Category Tools
Cloud Platforms Azure AWS GCP
Infrastructure as Code Terraform
Programming Languages Python JavaScript Java
Containerization & Orchestration Docker Kubernetes
CI/CD GitHub Actions
Design & Prototyping Figma

πŸ› οΈ What I Do

πŸ’» Cloud & DevOps Engineering

  • Automating Cloud Infrastructure (AWS, Azure, GCP) with Terraform & Kubernetes
  • Building CI/CD Pipelines with GitHub Actions, Jenkins, and ArgoCD
  • Securing cloud systems with IAM, HashiCorp Vault, and Kubernetes RBAC

πŸ€– MLOps & AI Integration

  • Managing ML Model Deployment with Kubeflow, MLflow, and AWS SageMaker
  • Optimizing AI workflows for scalability & performance in production
  • Data Engineering with Apache Spark, Kafka, and Airflow

πŸ”Ž Observability & Monitoring

  • Implementing Real-time Logging & Monitoring with Prometheus, Grafana, OpenTelemetry
  • Cost Optimization & Cloud Security Best Practices

🎯 Future Focus Areas:

  • Serverless AI Pipelines with AWS Lambda, S3, and DynamoDB
  • AI-driven Auto Scaling & Predictive Infrastructure Management
  • AI + DevOps Synergy β†’ Developing automated remediation for cloud failures

& Lot More


πŸ“Š GitHub Stats & Daily Streak




πŸ“« Connect with Me

GitHub Email


πŸ† Coding Profile & Competitive Programming

LeetCode Stats


Profile Views

⭐ "DevOps is the bridge between development and operations, and I am the architect of that bridge!" πŸš€

πŸ’‘ This README doesn't just highlight what I've doneβ€”it tells my story, passion, and future impact!

Digpal Singh Rathore πŸ’–

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    This project demonstrates how to build, containerize, and automatically deploy a FastAPI web application using a full CI/CD pipeline.

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    This repository tracks my journey of learning FastAPI by building a RESTful API with features like CRUD operations, database integration, authentication, cloud integration, and API testing.

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  4. Static-Website-AWS Static-Website-AWS Public

    The project demonstrates how to leverage S3 as a scalable, cost-effective solution for hosting static websites without the need for traditional web servers.

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  5. Learn Terraform Learn Terraform
    1
    1. terraform plan
    
    2
    --> The `terraform plan` command creates an execution plan, which lets you preview the changes that Terraform plans to make to your infrastructure.
    
    3
    --> Good practice to use this before upload or apply any configuration file
    
    4
    
                  
    5
    2. terraform validate 
    
  6. Docker Compose Gist Docker Compose Gist
    1
    version: "3.1"
    2
    
                  
    3
    volumes:
    4
      vol1:
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        name: "vol2"