CoderRange - Endless Programming Languages

🧠 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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🧠 You use databases every day. But do you really know how they think, store, index, replicate, recover?
This meetup is your full-access pass into the internal organs of the world’s 3 most powerful databases. 💾
### ⚙️ Session 1: MySQL Internals Unlocked
> Learn how MySQL evolved from a simple relational engine to a high-performance transactional beast.

• Storage Engines: InnoDB vs MyISAM
• How B+ Trees power indexes
• Redo Logs, Undo Logs & Doublewrite Buffer
• Query Optimizer Tricks – EXPLAIN plan analysis
• ACID + MVCC (multi-version concurrency control)
• Replication: Binlogs, GTIDs, Semi-sync
• Partitioning, Sharding Strategies
• Common MySQL Bottlenecks (with Fixes!)

### 🧠 Session 2: PostgreSQL – The Architect’s Database
> Postgres is not just SQL, it’s object-relational power with engineering-grade control.

• Process-based architecture: Background workers, WAL writer
• WAL Logs (Write-Ahead Logging) and crash recovery
• Query Planning: Cost-based optimizer
• PostGIS, JSONB & Full-Text Search internals
• Extensions & Hooks (pg_partman, TimescaleDB, Citus)
• MVCC & Vacuuming Mechanism
• Horizontal scaling with logical replication

🛠 Live Demo: Show query planning cost comparison + visualize WAL logs
### 🧬 Session 3: MongoDB – Document Magic Internals
> From flexible schemas to replica sets, MongoDB hides powerful structures beneath its JSON-style simplicity.

• BSON Storage Format vs JSON
• Storage Engine: WiredTiger internals
• Journaling and Checkpointing
• Replica Set: Oplog, Elections, Failover
• Sharding Architecture with Query Router (mongos)
• Indexing: Compound, Geospatial, Text
• Aggregation Pipeline Optimizations

🛠 Live Demo: Trigger a failover in a local replica set + aggregation perf tip
### 💎 Bonus Sessions:

• ✅ Build Your Own Storage Engine (in Python/Go)
• 🔐 Security Internals: Roles, Row-level Security, Field-Level Encryption
• 📊 Performance Engineering: How to benchmark DBs like a pro
• 🔁 Database Observability: Query logs, audit trails, slow query analysis

### 🎁 What Meetup Attendees Get:

• 🧾 PDF: Coderrange DB Internals Cheatsheet
• 🧪 CLI Labs to simulate replication, crash recovery, and WAL logging
• 💡 Decision Guide: “Which DB for which use case?”
• GitHub link with mini DB internals demo repo
• Access to “Build Your Own Query Optimizer” workshop invite

### 🎯 Ideal for:

• Backend & Full-stack Devs
• Data Engineers & Architects
• DBAs and Curious Founders
• Anyone scaling microservices & data platforms

Are you a Data Analyst with 2-8 years of experience craving the next-level skills to crack complex datasets, optimize pipelines, and deliver real-time business impact?
If yes, this meetup is your golden ticket!
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🎯 What we’re covering:
🚀 Extreme Real-Time Analytics & Data Internals

• Architecting lightning-fast data pipelines that don’t break under pressure
• Understanding streaming data platforms (Kafka, Spark Streaming) — beyond the basics
• Real-time anomaly detection, predictive analytics, and instant dashboards

📊 Advanced Data Modeling & Query Optimization

• Deep dive into SQL internals and performance tuning for massive datasets
• How to design scalable, maintainable data models for complex business scenarios
• Data warehouse vs. data lake vs. lakehouse: pros, cons, and use cases

🛠️ Tools & Techniques You’ll Love

• Leveraging Python & R for advanced stats and machine learning
• Automation hacks for ETL, data cleaning, and data quality checks
• Hands-on tips for using BI tools like Power BI, Tableau with real-time data

📈 What real employers expect from senior analysts

• Storytelling with data under extreme time constraints
• Cross-functional collaboration: influencing product and engineering with data
• From data wrangling to strategic decision-making: your complete playbook