Sandip Das

For quite some time I have been working on AI Agent Platform & here's what I found so far 👇

Before I go further, first understand what is AI Agent Platform:

An AI agent platform lets an LLM iteratively plan, call external tools, observe results, and refine its approach until a goal is met or a budget is exhausted. Unlike a single-shot prompt, an agent loop can execute code, query databases, browse the web, and call APIs, making reliability, security, and cost control first-class concerns. This page designs a platform capable of running thousands of concurrent agent sessions, each with isolated execution environments, multi-tier memory, deterministic step budgets, and full observability.

BUT most AI agent demos work fine. Most AI agent platforms break in production.
In my experience the model is rarely the problem. The stuff around it is.

Here's how I break an AI Agent Platform down:

🔹 Agent Orchestrator
Runs the plan → execute → observe loop. Checkpoints state to Redis after every step, so if a pod dies, the session resumes instead of starting from zero.

🔹 LLM Planner
Calls the LLM with a context-managed prompt (trimmed history, only the relevant tool specs) and returns a structured plan. Structured, not free text, so the orchestrator can validate it before anything runs.

🔹 Step Budget Enforcer
Atomically tracks steps, tokens and wall-clock time per session. Hit a limit, the session stops cleanly. This is what saves you from the agent that loops 400 times at 3 AM on your API bill.

🔹 Tool Registry
Stores tool definitions, JSON schemas, execution backends and permission scopes. The agent can only call registered tools, with validated inputs, and only what that session is allowed to touch.

Skip any one of these and you'll feel it:
No checkpoints → lost sessions
No structured plans → unpredictable actions
No budgets → runaway cost
No registry → an agent with more access than it should have

What would you add? I'd put tracing and human-in-the-loop approvals right next to these.

Full write-up with the architecture diagram 👇
app.learnxops.com/system-designs/ai-agent-platform

#MLOps #AI #LLM #MachineLearning #SystemDesign

2 weeks ago | [YT] | 5

Sandip Das

217 Kubernetes ONLY labs released: app.learnxops.com/labs/kubernetes

#kubernetes #labs

4 months ago | [YT] | 12

Sandip Das

Learner’s: Sandip dada , please add labs so we can practice what we learn from you
Me: Ok, I will get that done 👍
2 weeks fast towards , now we have Learn X Ops Labs , more than 50 labs across Linux, Docker, Networking , AI, MLOps and adding today AWS & Azure Labs ( best part you will be able to practice cloud without cloud account from your local machine )

I personally made sure labs works on every kind of machine ( as you can see from picture 😎)

Try it out today 👍 : app.learnxops.com/download

Repost this to spread the good news!

#cloud #devops #mlops #labs

4 months ago | [YT] | 7

Sandip Das

I used my idle raspberry Pi devices as scraping and data collector agent and via Local AI filtered and store most relevant 1303 Cloud , DevOps , MLOps , Platform Engineering & Software Engineering Jobs!

Now this job listing page is live! find the job list link here: app.learnxops.com/jobs



#joblisting #ai #cloud #devops #mlops #platformengineering

4 months ago | [YT] | 9

Sandip Das

Just release some awesome learn for LearnXops Members:
The AI Projects, under AI & Cloud & DevOps: app.learnxops.com/ai-for-cloud-devops-engineers-se…
Awesome Resume Builder (with AI) with ATS Score analyzer : app.learnxops.com/resume-builder

Finally these ware the things I was working on and finally done , now I will continue working on videos and courses

4 months ago | [YT] | 4

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

10 months 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

10 months ago | [YT] | 20

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

11 months ago (edited) | [YT] | 12

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

1 year 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

1 year ago | [YT] | 14