Sandip Das will help you learn DevOps, Cloud (AWS and Other Cloud), Programming (Python, JavaScript, Go, and other programming languages), career tips, and so on!

Sandip Das is recognized as AWS Hero, and Hasicorp Ambassador, and creates new video tutorials every week, which mainly include Cloud Service Providers, DevOps Tools concepts & hands-on tutorials, and Programming Topics.

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Sandip Das

I spent 2+ weeks designing and building a real-world, production-ready setup for Amazon EKS with auto-scaling using Karpenter — and I’m giving it away for FREE.

🔥 What’s inside this project?
✔️ Full-stack CRUD application
✔️ Deployed on Amazon EKS
✔️ Karpenter-based auto-scaling
✔️ Smart mix of Spot + On-Demand nodes
✔️ Load balancing, resilience & cost optimization
✔️ Infrastructure designed the way it’s done in production, not tutorials

💡 Why this matters

Most engineers struggle with:
Correctly configuring Spot + On-Demand together
Avoiding node churn & interruptions
Making Karpenter work reliably in real workloads

This project solves those exact problems with a clean, practical setup you can actually reuse.

👉 Try it yourself (FREE):
🔗 www.learnxops.com/aws-project-challenge-9-deploy-a…

If you’re working with EKS, Kubernetes, or Cloud cost optimization, this is for you.

💬 Let me know in the comments:
Are you using Karpenter or Cluster Autoscaler in production?


Cheers,
Sandip Das

hashtag#AWS hashtag#EKS hashtag#Kubernetes hashtag#Karpenter hashtag#DevOps hashtag#CloudEngineering hashtag#AutoScaling hashtag#LearnXops

1 month ago | [YT] | 18

Sandip Das

Project: 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐒𝐲𝐬𝐭𝐞𝐦 𝐰𝐢𝐭𝐡 𝐀𝐖𝐒 𝐅𝐚𝐫𝐠𝐚𝐭𝐞, 𝐂𝐞𝐥𝐞𝐫𝐲, 𝐒𝐐𝐒 & 𝐓𝐞𝐱𝐭𝐫𝐚𝐜𝐭
Link: www.learnxops.com/aws-project-challenge-8-intellig…

‪@learnTechWithSandip‬

#aws #projects

2 months ago | [YT] | 19

Sandip Das

Hi @everyone,

I’m thrilled to share some great news 🎉

Over the last two years, I’ve been silently pursuing my Master of Science and doing specialization on Cloud, DevOps, and AI/ML — and I’m happy to say it’s now completed!

Balancing this journey alongside content creation, client project deliveries, and teaching wasn’t easy — but it’s been incredibly rewarding.

Six years ago, when I first tried learning AI & ML, I completely failed. I genuinely believed it wasn’t for me. But two years back, I decided — it’s now or never. I treated it as a personal challenge, took it up as a Master’s pursuit, and today, I can proudly say I made it through.

Many of the AI/ML and MLOps projects you’ve seen on LearnXops came directly from the clarity and foundation I gained through this journey. My goal has always been to simplify complex concepts so anyone can learn them with confidence.

📍Last week, I was on an onsite trip in Hyderabad, which delayed the Advanced AWS Projects release — but this week, it’s happening! I’ll be releasing 5 Real-Life Advanced AWS Projects, wrapping up 30 Days of AWS (part of 90 Days of Cloud), and kicking off 30 Days of Azure (same format — 20 Days Theory + 10 Days Projects).

Alongside that, I’ll also be launching new Case Studies, Projects, and eBooks — exclusively for LearnXops Premium Members.

🎁 Special Announcement:
To celebrate this milestone, I’m introducing the LearnXops Lifetime Membership — with 50% off exclusively for all existing LearnXops Premium Members.
Use code ProMembers50PercentOff or use this pre-applied link:
👉 topmate.io/sandip_das_official/1756332?coupon_code…

This is my way of showing gratitude and commitment to delivering never-ending quality content through LearnXops.

Thank you all for your continuous support and encouragement 🙌
Here’s to learning, growing, and building — together.

Also, started making YouTube videos, stay tuned!

Regards,
‪@learnTechWithSandip‬

2 months ago (edited) | [YT] | 11

Sandip Das

Durga Pooja is one of our biggest festivals, and I want to make it special for all my followers and well-wishers, especially those who are Learning from LearnXops.com (it's limited to 100 persons, so get it FAST! )
Use code "DurgaPooja25" to get 25% off on LearnXops Premium Plan
This is only applicable when you use the "CashFree" Payment Gateway
www.learnxops.com/membership-payment/

What about the international Members (or actually anyone)?
You can use the Topmate to pay and avail this offer: topmate.io/sandip_das_official/1513567?coupon_code…

Cheers,
‪@learnTechWithSandip‬

4 months ago | [YT] | 5

Sandip Das

If you would like to start learning MLOps in 2025, start with this :

Day 1: Intro to MLOps: ML Meets DevOps: lxop.in/day1

Day 2: MLOps Tools Landscape: lxop.in/day2

Day 3: Data Versioning with DVC: lxop.in/day3

Day 4: Reproducible ML environments (Conda & Docker): lxop.in/day4

Day 5: Feature Engineering & Feature Stores: lxop.in/day5

Day 6: Training ML Models with Scikit-learn & TensorFlow: lxop.in/day6

Day 7: Model Experiment Tracking with MLflow: lxop.in/day7

Day 8: Model Evaluation & Metrics: lxop.in/day8

Day 9: ML Pipelines with Kubeflow Pipelines - lxop.in/day9

Day 10: Serving ML Models with FastAPI & Flask: lxop.in/day10

Day 11: Packaging Models with Docker: lxop.in/day11

Day 12: CI/CD for ML with GitHub Actions: lxop.in/day12

Day 13: ML Model Deployment: lxop.in/day13

Day 14: Data Drift & ML Model Drift Detection : lxop.in/day14

Day 15: Automated Retraining ML Pipelines: lxop.in/day15

Day 16: Security in MLOps: lxop.in/day16

Day 17: Explainable AI (XAI) in Production: lxop.in/day17

Day 18: ML Model Governance & Compliance: lxop.in/day18

Day 19: Monitoring ML Systems in Production: lxop.in/day19

Day 20: Model Registry: lxop.in/day20

Day 21: Scaling ML Model Inference with Kubernetes: lxop.in/day21

Day 22: MLOps with ML Platforms: lxop.in/day22

Day 23: Managing LLMs in Production: lxop.in/day23

Day 24: Agentic AI & RAG: lxop.in/day24

Day 25: MCP Explained for MLOps Engineers: lxop.in/day25

Day 26: Project: End-to-End MLOps Pipeline: lxop.in/day26

Day 27: Model Deployment with Serverless Architectures: lxop.in/day27

Day 28: Cost & Performance Tuning in MLOps: lxop.in/day28

Day 29: Disaster Recovery & HA for ML Systems: lxop.in/day29

Day 30: MLOps Interview Question & Answers: lxop.in/day30



🔁 Consider a Repost if this is helpful



Cheers,

‪@learnTechWithSandip‬

#mlops #devops #cloud

5 months ago | [YT] | 14

Sandip Das

Disaster Recovery & High Availability for ML Systems
Read: www.learnxops.com/disaster-recovery-high-availabil…

5 months ago | [YT] | 7

Sandip Das

Cost Optimization & Performance Tuning in MLOps
Read here => www.learnxops.com/cost-optimization-performance-tu…

5 months ago | [YT] | 9

Sandip Das

End-to-End MLOps Project for DevOps Engineers!
If you are serious about learning MLOps or ML as a DevOps Engineer, this one project you MUST try!
Get it here: www.learnxops.com/project-end-to-end-mlops-pipelin…

This is a Customer Churn Prediction Project!

In simple terms, “churn” means a customer leaves — cancels their subscription, switches to a competitor, or stops making purchases. Churn prediction uses historical data (e.g., usage patterns, support interactions, purchase history, demographics) and machine learning/statistical models to forecast the likelihood of each customer leaving.

This single project has:

Full Python Code to generate the required data for ML Model Training + Model Training Script (using Random Forests ) + Model Tracking in MLFlow

Generated ML Model Serving via FastAPI (& Prediction API)

Performance Tracking & Drift Detection

Monitoring via Prometheus & Grafana

Containerization via Docker

Full CI/CD via Github Actions!

The BEST part, everything you will be doing via simple scripts that I have designed for you, you will find it as MOST simplest yet a full end-to-end project!

Find the Project Link in the first comment!

🔃 Consider reposting for better reach!

Cheers,
‪@learnTechWithSandip‬


#mlops #mlopsproject #project #free

5 months ago | [YT] | 18

Sandip Das

Model Context Protocol (MCP) Explained for MLOps Engineers
Read here: www.learnxops.com/model-context-protocol-mcp-expla…

5 months ago | [YT] | 7

Sandip Das

Generative models like GPT-4 and LLaMA can generate fluent text but often hallucinate, lack current knowledge, and have no long-term memory. Two complementary strategies are emerging to make these systems more robust in production:
Agentic AI: Empowers models with reasoning, planning, memory, and tool usage.
Retrieval-Augmented Generation (RAG): Grounds model outputs in external knowledge sources.

Continue reading to learn about Agentic AI & RAG in Simple Words:
=> www.learnxops.com/agentic-ai-retrieval-augmented-g…

🔃 Consider reposting this post if you found it useful

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Cheers,
‪@learnTechWithSandip‬

5 months ago | [YT] | 13