@sumanprasad
AWS Community Builder ❤| Cloud DevOps Engineer | Technical Writer✍️| 330+ Days of Consistent Community Contribution🌟|AWS Cloud Specialist |
👋 Hello! I'm passionate about DevOps and have over 1+ years of experience in the field. I'm proficient in a variety of cutting-edge technologies and always motivated to expand my knowledge and skills. Let's connect and grow together!
SKILLS:
🔹 Languages & Runtimes: Python, Shell Scripting, HCL, YAML 🔹 Cloud Technologies: AWS, Microsoft Azure, GCP 🔹 Infrastructure Tools: Docker, Terraform, AWS CloudFormation 🔹 Other Tools: Linux, Git and GitHub Actions, Jenkins, Jira, GitLab (beginner), Docker, AWS DevOps 🔹 Web Development: HTML, CSS, Bootstrap, Python, SQL
Job & Responsibilities:
🚀 Improved development efficiency by implementing CI/CD pipelines, resulting in a 30% reduction in deployment time on the test server. 🔒 Strengthened deployment and testing reliability by utilizing Docker containers and optimizing Dockerfile, reducing development issues on the test server by 20%. ⚙️ Automated S3 bucket log creation with Shell scripting, eliminating 100% of manual search and saving 2 hours per week. 📅 Scheduled EC2 instance start/stop using Lambda functions and Event Bridge, leading to a 25% decrease in infrastructure costs. 🔧 Utilized AWS, Linux, Python, Docker, Shell scripting, Terraform, Jenkins Pipelines, and automation to streamline workflows and improve overall system performance.
I'm very detail-oriented and possess strong written and verbal communication skills. As a high performer with a possibility mindset, I strive to solve problems using efficient approaches.
Let's Connect & Grow:
If you find my profile suitable for the role you are searching for, please feel free to reach out to me at sumanprasad9766@gmail.com.
📍 Available for Collaboration 📍 Available to Serve Open Source Contribution 📍 Available to Mentor beginner & solve their doubts...
Feb 9 · 3 min read · Train a small ML model Expose it as an API Package it with Docker Run it like a real production service Step 1: Create a simple model and save it Create a file called train.py from sklearn.datasets import load_iris from sklearn.ensemble import ...
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Feb 2 · 5 min read · So far you’ve learned: Day 1 → What MLOps is Day 2 → ML lifecycle Day 3 → Data engineering basics Day 4 → Data drift and model decay Day 5 → Experiment tracking and model registry Today we answer a key question: How does a model that was trained on a...
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Jan 26 · 5 min read · So far, you’ve learned: Day 1 → What MLOps is and why it matters Day 2 → ML lifecycle Day 3 → Data engineering basics Day 4 → Data drift and data quality Today we answer another key question: Why do companies carefully track every ML experiment, eve...
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Jan 18 · 4 min read · So far: Day 1 → What MLOps is and why it matters Day 2 → The ML lifecycle from idea to production Day 3 → Data engineering basics and data pipelines Today we answer a very important question: Why do ML models get worse over time, even if they were ac...
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