3. Data, Science & AI

Comprehensive guide to ELT modeling patterns, dimensional modeling, and data warehouse design for effective data processing and storage.

Generates optimized features from raw data (numerical, categorical, text, time-based) to enhance machine learning model performance.

Step-by-step guide for locally deploying large language models using Ollama, vLLM, and llama.cpp.

Optimizes context formatting for LLMs to ensure conciseness, structure, and efficient token usage in AI workflows.

Manages ground truth datasets for AI model evaluation, including annotation, quality control, and versioning to ensure data integrity and reproducibility.

Implementation of data quality rules to validate integrity, consistency, and accuracy in data processing workflows.

Step-by-step guide for implementing Optical Character Recognition (OCR) with Tesseract in Python to extract text from images.

Maps data flow from source to destination, enabling transparency, impact analysis, and efficient troubleshooting in data pipelines.

Automates detection of model performance degradation and data drift, triggering intelligent retraining to maintain model accuracy.

Optimizes token usage in LLM interactions to reduce costs and improve efficiency in AI applications.

Provides design patterns for building autonomous AI agents capable of reasoning, planning, and task execution in LLM-based systems.

Transforms raw data into actionable insights using BI tools for reporting, analytics, and visualization to support data-driven decisions.

Creates clear and accurate data visualizations using charts and graphs, adhering to Tufte and Few principles for effective insight communication.

Detects, measures, and reduces algorithmic bias in machine learning models to ensure fair and equitable AI outcomes.

Provides marketing analytics for KPI tracking, dashboard reporting, attribution analysis, and campaign performance optimization.

Ensures data accuracy, completeness, and reliability across data pipelines using validation techniques and monitoring tools.

Processes and generates multimedia content including audio, images, videos, and documents using Google Gemini API for advanced vision and multimodal capabilities.

Enables semantic and similarity search implementations using vector embeddings and vector databases for AI-driven information retrieval.

Provides strategic guidelines for effective context retrieval in AI systems, including selection criteria and ranking techniques for optimal performance.

Step-by-step guide for image preprocessing in OCR and computer vision using Python and OpenCV.

Generates efficient SQL queries for data analysis using aggregations, window functions, and CTEs to extract insights from large datasets.

Analyzes user journey conversion rates by visualizing sequential steps to identify drop-off points and optimize each stage for improved retention and conversion.

Standardizes text-based interactions with LLMs, covering prompt engineering, response formatting, and text processing patterns for consistent model interactions.

Provides expert guidance on optimizing database queries using EXPLAIN analysis, indexing strategies, and eliminating N+1 query issues.