"I live with data, for data — because everything is about data."
mindmap
root((Data Expert))
Data Science & ML
CRISP-DM
Statistical Analysis
Deep Learning
MLOps & LLMOps
Database Architecture
PostgreSQL
MongoDB
DynamoDB
Vector DBs
MLOps Infrastructure
Airflow
MLflow
Docker
Monitoring
Best Practices
SOLID Principles
Clean Code
12-Factor Apps
Documentation
Primary Databases
| Category | Technologies & Expertise |
|---|---|
| Relational |  -CC2927?style=flat-square&logo=microsoft-sql-server){kind=icon} |
| NoSQL |   |
| Vector |   |
| Cache/Queue |  |
| Data Warehouses |    |
Database Tools & Management
| Category | Tools |
|---|---|
| Primary UI Tools |   |
| Management Tools |   |
| Cloud Management |   |
| CLI Tools |   |
Learning & Exploration
| Category | Technologies |
|---|---|
| Time Series |   |
| Graph |  |
| Search & Analytics |   |
| Distributed |   |
Database selection is always problem-driven! Primarily focused on data science and analytics use cases.
Data Processing & Analysis
| Category | Technologies |
|---|---|
| Core Processing |     |
| Big Data |    |
| Machine Learning |    |
| Deep Learning |   |
| Statistical Analysis |    |
| Time Series |   |
| Survival Analysis |  |
Data Visualization & Applications
| Category | Technologies |
|---|---|
| Interactive Viz |   |
| Web Applications |   |
| Static Plotting |   |
| Business Intelligence |   |
Model Development & Experimentation
| Category | Technologies & Status |
|---|---|
| Experiment Tracking |   |
| Hyperparameter Tuning |   |
| Version Control |   |
| Model Registry |  |
Deployment & Serving
| Category | Technologies & Status |
|---|---|
| API Development |    |
| Containerization |   |
| Model Serving |   |
| Cloud Deployment |    |
LLMOps & AI Infrastructure
| Category | Technologies & Status |
|---|---|
| LLM Monitoring |   |
| LLM Frameworks |   |
| Vector Databases |  |
| Model Hosting |   |
Monitoring & Observability
| Category | Technologies & Status |
|---|---|
| Metrics & Dashboards |  |
| System Monitoring |   |
| Application Monitoring |   |
| Tracing |   |
| Alerting |   |
Workflow Orchestration
| Category | Technologies & Status |
|---|---|
| Primary Orchestrator |  |
| Learning/Exploring |   |
| Low-Code Solutions |  |
| Schedulers |   |
Cloud Platforms
| Category | Technologies & Status |
|---|---|
| Primary Cloud |  |
| Secondary Cloud |  |
| Self-Hosted |  |
Programming Languages
| Language | Proficiency | Use Cases |
|---|---|---|
 |
Expert | Data Science, MLOps, Backend APIs |
 |
Expert | Database queries, analytics, ETL |
 |
Beginner | Microservices, CLI tools |
 |
Intermediate | Statistical analysis (rarely used) |
Statistical Approach
class StatisticalPhilosophy:
"""Bayesian thinking, frequentist validation."""
def __init__(self):
self.statistical_practices = {
"hypothesis_testing": {
"approach": "Bayesian-first, frequentist validation",
"tools": ["scipy.stats", "statsmodels", "pymc"],
"principles": [
"Effect size over p-values",
"Confidence intervals",
"Power analysis",
"Multiple testing correction"
]
},
"model_evaluation": {
"cross_validation": ["time-series-split", "nested-cv"],
"metrics": ["business-aligned", "statistical-rigor"],
"validation": ["out-of-time", "out-of-sample"]
},
"experimental_design": {
"methods": [
"A/B Testing",
"Multi-armed bandits",
"Factorial designs"
],
"considerations": [
"Sample size calculation",
"Randomization",
"Control groups"
]
}
}
def favorite_template(self):
return "cookiecutter-data-science by @drivendataorg"Project Structure Philosophy
📁 project_name/
├── 📁 data/ # Data files (git-ignored, DVC-tracked)
│ ├── 📁 raw/ # Immutable raw data
│ ├── 📁 processed/ # Cleaned, transformed data
│ └── 📁 features/ # Feature engineering outputs
├── 📁 notebooks/ # Jupyter notebooks (EDA, experiments)
│ ├── 📝 00_eda.ipynb
│ └── 📝 01_modeling.ipynb
├── 📁 src/ # Source code
│ ├── 📁 data/ # Data processing
│ ├── 📁 features/ # Feature engineering
│ ├── 📁 models/ # Model training and inference
│ └── 📁 visualization/# Plotting and dashboards
├── 📁 tests/ # Test files
├── 📁 configs/ # Configuration files
├── 📁 docs/ # Documentation
├── 📁 monitoring/ # Grafana dashboards, alerts
├── 📁 deployment/ # Docker, K8s, cloud configs
├── 📄 .env.example # Environment variables template
├── 📄 .gitignore
├── 📄 pyproject.toml # Project metadata and dependencies
├── 📄 README.md # Project documentation
├── 📄 Dockerfile # Container definition
└── 📄 docker-compose.yml # Local development stack
Code Quality Standards
| Category | Tools & Practices |
|---|---|
| Linting |    |
| Type Checking |  |
| Testing |   |
| Documentation |   |
| Pre-commit |  |
My go-to template for structured data science projects!
My favorite game is... building data pipelines so powerful that even the final boss (your data chaos) gets defeated before the first turn!
Currently working on a comprehensive video games database with ML-powered recommendation system!
🕹️ Video Games Database
Current Features:
- Comprehensive game metadata collection
- User rating prediction models
- Recommendation engine using collaborative filtering
- Real-time data pipeline with Airflow
- Interactive Streamlit dashboard
- Self-hosted MongoDB cluster
- Grafana monitoring for data quality
Tech Stack: Python, MongoDB, Airflow, MLflow, Streamlit, Docker
🎯 2025 Goals & Progress
- Complete IBM Data Scientist Certification ✅
- Deploy 5 production ML models with full monitoring
- Master Grafana & Prometheus for ML observability
- Contribute to 3+ open-source MLOps projects
- Complete comprehensive video games database project
- Learn Go language fundamentals
- Implement end-to-end LLMOps pipeline
- Write 10 technical blog posts about MLOps
Current Focus: Building robust, self-hosted MLOps infrastructure and mastering LLMOps practices.
Last updated: 2025-08-01 13:27:52 UTC by @gkhnelbstn
✨ Always learning, always building, always optimizing ✨
✨ Always learning, always building, always optimizing ✨