CoderRange - AI , Big data , Data Science !.

### 🚀 “The Intelligence Triangle” – Innovation Meetup by Coderrange

> ✨ 3 roles. 3 mindsets. 1 future powered by AI.
> This isn’t just a talk. It’s a data evolution battle, live demos, open debates, and surprise GenAI workflows that redefine your role.

### 👥 Who Is This For?

• 🔍 Data Analysts – Excel to SQL to Power BI wizards wondering: “Where do I go next?”
• 🧠 Data Scientists – ML/NLP pros exploring LLMs and advanced data ops
• 🤖 GenAI Builders – Prompt engineers, Langchain users, vector DB fans and AI agents creators

#### 📈 Session Case Studies – Real World, Real Impact

1. Analyst → Built live dashboards for 5M users in fintech (Power BI + DAX tricks)
2. Data Scientist → Deployed fraud detection using XGBoost in production
3. GenAI Dev → Built RAG chatbot to automate internal support for 300+ teams

#### 💎 Bonus Session: "Your Career in 2025+ in the Age of GenAI"

• How analysts become AI Product Managers
• Why scientists must learn vector search
• Why devs must understand business KPIs

### 🎁 FREE for All Attendees:

• 🧾 Coderrange AI CodeSheet: SQL-to-LLM cheat sheet, Python + Pandas tips, RAG boilerplate
• 🎓 Career Map PDF: Analyst → Scientist → GenAI Builder path
• 🧠 Access to post-event GitHub Repos

You don’t want to be replaced. You want to evolve.
This event helps you understand not only what’s coming – but how to ride it like a pro.

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🧠 Most devs write Java. Few know how Java thinks.

This Meetup uncovers the deep, internal engineering marvels inside the Java ecosystem that make it scalable, secure, and insanely robust.

### 🔍 What Most Devs Miss:

You use Java daily...
But do you know:

• 🤯 How garbage collection predicts memory patterns?
• 🔐 How Java uses internal cryptographic APIs to secure billions of financial transactions?
• 🧬 How JIT and GraalVM optimize your code while it runs?
• 🛠️ What’s the magic behind Spring’s dependency injection?
• ⚙️ How annotations turn into bytecode transformations at runtime?

### 🧠 What Developers Will Learn:

• 🧬 JVM Deep Dive: Memory, GC, and JIT – Visualized Live
• 💡 How Java powers high-frequency trading systems & Android apps alike
• ⚔️ Reflection vs Annotation vs Proxy – what's REALLY happening?
• 📦 Build your own Java-based lightweight DI framework in 15 minutes
• 🚀 Project Loom, Panama, Valhalla – The Next 10 Years of Java

### 📘 You’ll Walk Away With:

• 🧾 Coderrange Java Internal Innovation Sheet
• GC types cheat sheet
• JIT stages explained
• Bytecode disassembler tool
• GraalVM AOT CLI use
• 🎁 Access to live demo code:
• Classloader tricks
• DI from scratch
• Thread tuning with Loom
• 💬 Invite to our “JVM Engineers Guild” Discord

### 👥 Who Should Join:

• Java developers (2-10 YOE)
• Spring Boot lovers curious about internals
• JVM-based language fans (Kotlin, Scala)
• Devs who want performance + architecture edge

We’re going beyond frameworks — we’re going into the core 🔥

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Are you ready to explore the world of Cloud Computing with AWS? Join our power-packed one-day workshop designed for beginners to get hands-on experience with Amazon Web Services (AWS)—the world’s leading cloud platform!

🧠 Ever wondered how ML models go from Jupyter Notebooks to powering recommendations for millions?

🛠️ This Meetup pulls back the curtain on the full MLOps lifecycle – not in theory, but through a real-world production case study.

### 🔥 Case Study: Real-Time Fraud Detection System in a Fintech Platform

(A real MLOps implementation inspired by industry practices at companies like PayPal, Razorpay, and Stripe)

#### ⚡ Problem:

Catch fraudulent transactions (money laundering, account takeover, bot attacks) in real-time — with millions of events coming in per day.

### 🔥 Live Demo :

• How a fraudulent transaction gets scored in real-time
• Auto model promotion from staging to production
• Detecting model drift using live dashboards
• Retraining trigger via scheduled job (Airflow)

### 🎁 You’ll Get:

• 🧾 Coderrange MLOps CaseSheet PDF: Tools, pipelines, CLI commands
• ⚡ GitHub mini-repo: Sample CI/CD for ML model
• 🔄 YAML templates for model deployment with KServe
• 🧠 Prompt template for evaluating models using LLM (bonus!)

### 👥 Meetup Audience Fit:

• ML Engineers & Data Scientists
• Backend Devs curious about ML in prod
• DevOps & Platform Engineers
• Anyone building AI/ML powered apps

MLOps isn't just DevOps + ML. It's how AI scales.

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