• Advancing my expertise in MLOps & Cloud ML Pipelines
• Exploring Prompt Engineering and Generative AI applications
• Building automated analytics dashboards with Streamlit & FastAPI

  Python · SQL · FastAPI · Streamlit · TensorFlow · Keras · Scikit-learn · Airflow · Docker · GitHub Actions · AWS

Python

Anaconda

Bash

Git

GitHub

GitHub Actions

Jupyter

NumPy

Pandas

Scikit-learn

Statsmodels

TensorFlow

Keras

PyTorch

Docker

AWS

Ubuntu

Linux

MySQL

PostgreSQL

MongoDB

SQLite

Redis

HTML

CSS

JavaScript

TypeScript

Flask

FastAPI

React

Angular

Tailwind

Matplotlib

Plotly

Seaborn

Excel

Tableau

VS Code

GitHub Copilot

Markdown

Notion

Postman

Airflow

Google Workspace

Slack

Microsoft Teams

• Microservices Architecture: Designing scalable and resilient systems.
• RESTful APIs: Building clean and predictable web services.

A modern, real-time chat application showcasing enterprise-level architecture and best practices.

Core foundations for analytical, ML, and full-stack work

• 🐍 Python (core + advanced: OOP, async, decorators, testing, logging)
• 💻 SQL / MySQL / PostgreSQL (DDL, DML, DQL, joins, subqueries, CTEs)
• ☕ Java (fundamentals, OOP, data structures)
• 🌐 JavaScript (ES6+)
• 🧩 HTML5 / CSS3
• 🐚 Bash / Shell Scripting

End-to-end DSML workflow — from data wrangling to model deployment

• NumPy, Pandas, Scikit-learn
• TensorFlow, PyTorch
• Matplotlib, Seaborn, Plotly, Tableau, Power BI
• Statsmodels, SciPy
• EDA (Exploratory Data Analysis) and Feature Engineering
• Model Evaluation (ROC-AUC, confusion matrices, precision/recall, etc.)
• Cross-Validation & Hyperparameter Tuning
• Supervised Learning: Regression, Classification
• Unsupervised Learning: Clustering (K-Means, GMM, DBSCAN, HAC)
• Dimensionality Reduction: PCA, t-SNE
• Advanced ML: XGBoost, LightGBM, CatBoost
• NLP: Tokenization, Word Embeddings, Transformers, Prompt Engineering
• Time Series: ARIMA/SARIMA, Prophet, LSTM forecasting
• Recommender Systems
• Feature Importance / Model Explainability: SHAP, Permutation Importance

Modern ML pipeline automation, deployment, and monitoring

• Docker, Kubernetes
• Git, GitHub Actions (CI/CD)
• Apache Airflow (workflow orchestration)
• MLflow, DVC (experiment tracking & versioning)
• EvidentlyAI, Prometheus, Grafana (model monitoring)
• Seldon, Istio, FastAPI (model serving)
• Terraform (IaC basics)
• Kubeflow, Vertex AI, SageMaker
• Concept Drift Detection, A/B Testing, Canary Deployments

Scalable compute, data storage, and ML deployment environments

• AWS (S3, Lambda, EC2, SageMaker)
• Google Cloud (GCP) (GKE, Vertex AI, BigQuery)
• Microsoft Azure (Basics: ML Studio, App Service)
• Heroku (App deployment)
• Streamlit Cloud / Hugging Face Spaces (ML app hosting)

Handling structured, unstructured, and streaming data

• SQL (MySQL, PostgreSQL, SQLite)
• NoSQL (MongoDB, Redis)
• Data Modeling & Normalization
• ETL Pipelines & Data Pipelines
• API Integration & Data Ingestion
• Data Warehousing Concepts
• Parquet / Feather / Arrow formats

Building interactive dashboards, APIs, and production-ready systems

• FastAPI, Flask (Python backends)
• React.js (frontend)
• Dash, Streamlit, Voila
• RESTful API Design & JWT Auth
• Asynchronous Programming (asyncio, aiohttp)
• Template Rendering (Jinja2)
• CORS, OAuth2, Middleware handling
• Postman, OpenAPI / Swagger

Transforming data into actionable insights and interactive stories

• Matplotlib, Seaborn, Plotly, Altair
• Dashboards: Streamlit, Dash, Tableau
• Power BI, Excel Analytics
• Storytelling with Data, KPIs & Metrics Design
• Automated Reporting (Papermill, nbconvert)

Analytical foundation for ML, inference, and optimization

• Descriptive Statistics (mean, variance, quantiles)
• Inferential Statistics (t-tests, ANOVA, chi-square)
• Regression Analysis (simple, multiple, logistic)
• Probability Distributions (Normal, Binomial, Poisson)
• Bayesian Inference, A/B Testing, Hypothesis Testing
• Linear Algebra: Vectors, Matrices, Eigenvalues
• Calculus: Differentiation, Optimization, Gradient Descent

Continuous improvement and reproducibility practices

• GitHub Actions (CI/CD pipelines)
• Makefiles, Shell automation
• Logging, Debugging, Unit Testing (pytest, unittest)
• Version Control, Branching strategies
• Papermill, nbconvert, cron / scheduler
• Notion, Markdown, Documentation Writing

Developer environments and collaboration tools

• VS Code, JupyterLab, PyCharm
• Anaconda / Miniconda
• Colab, Kaggle Notebooks
• Notion, Trello, Slack

Enterprise-grade practices for robust ML workflows

• Git / GitHub (PRs, branches, releases)
• Code Review Standards
• Data Versioning (DVC)
• Model Registry (MLflow / Vertex AI)
• CI/CD Workflows
• Documentation & Reproducibility Best Practices

Advanced and cutting-edge focus areas explored

• Large Language Models (LLMs) — Prompt Engineering, Chain-of-Thought, LangChain
• Self-Supervised Learning (SimCLR, BYOL)
• Vision: CNNs, Transfer Learning, Autoencoders
• NLP: Sentiment Analysis, Text Summarization, NER
• Generative AI: ChatGPT, OpenAI API integration
• Reinforcement Learning (Introductory)

Essential to data-driven and engineering mindset

• Analytical Thinking & Problem Solving
• Communication & Documentation Clarity
• End-to-End Ownership (Design → Deploy → Monitor)
• Team Collaboration (GitHub, Agile workflows)
• Technical Writing & Knowledge Sharing
• Continuous Learning & Automation Mindset