My new Portfolio App is LIVE! Frontend: Vibe coding (AI) Backend: AWS (S3+CloudFront) + Terraform + GHA
Centralising my work for the community.
Devs, let me know what you think! π
https://t.co/eHn1wAiJtX
#DevOps#AWS#Portfolio#VibeCoding#2025WrapUp
Kubernetes autoscaling doesn't always have to be about CPU and memory.
I explored KEDA + Kubernetes + Redis in a hands-on setup to understand event-driven autoscaling.
Redis events β KEDA β Kubernetes workloads β automatic scaling π
#Kubernetes
https://t.co/Yc7OBfeTIB
Day 11 of MLOps: Simplifying ML Model Serving with KServe
Installed KServe, deployed ML models using InferenceService, and explored production-style model serving on Kubernetes.
#MLOps#KServe#Kubernetes#AI
https://t.co/db5D8WmgQ3
Day 10 of MLOps: Why Modern ML Teams Deploy Models on Kubernetes Instead of VMs
Built, containerised, and deployed an ML model on AWS EKS using Docker, Terraform & Kubernetes.
#MLOps#Kubernetes#AWS#Docker#MachineLearning
Blog below π
https://t.co/pzxaG0wWnW
Day 08 of my MLOps journey π
Deployed and served an ML model locally using Flask APIs while understanding where local deployment fits in the MLOps lifecycle.
GitHub: https://t.co/5whoPpyh7n
#MLOps#DevOps#Cloud#AI
Blog below π
https://t.co/7uyUCgMttV
@dhruv_rathee My plan for Saturday: 06 June
Upskilling and spending some quality time with friends and family
P.S.: Education minister must resign
Spend your invaluable time wisely π
If we have to try harder to win the ODI WC.
IMO, @BCCI should reconsider the captaincy and give it to someone who can play well under pressure
BTW GT is the second biggest choker after RSA
Day 05 of my MLOps journey π
Learned how DVC, Git & S3 work together for ML dataset versioning and reproducibility.
#MLOps#DevOps#Cloud#AI
Blog link below π
https://t.co/gXZQrV4qHj
Honoured to be renewed as an #AWS Community Builder for the 4th consecutive year πβοΈ
Grateful for the opportunities, learning, community, and connections this program has brought over the years.
Excited for another year of building, sharing, and growing in cloud & DevOps π
Day 04 of my MLOps journey π
Deployed a trained ML model using Flask & Docker while learning how ML services work in production.
#MLOps#DevOps#Cloud#AI
Blog link below π
https://t.co/B6AO1nVboc