3. Data, Science & AI

Expertly designs enterprise data warehouses using star schema, dimensional modeling, and slowly changing dimensions with best practices.

Provides BI fundamentals: metric definition, KPI calculation, dimensional modeling, dashboard optimization, and data storytelling with 40+ examples.

Extracts and structures PDF text into title/paragraph format for document summarization and data extraction tasks.

Administers and develops MySQL databases, including schema design, query optimization, and user access management.

Enables building and training machine learning models using Python libraries such as scikit-learn, PyTorch, and TensorFlow.

Provides utilities for Redis advanced data types including Bitmaps, HyperLogLog, Streams, and Geospatial indexes to solve complex data processing challenges.

Provides standardized patterns and templates to enhance communication with large language models (LLMs).

Enables efficient data manipulation, analysis, and visualization using Pandas, NumPy, and Matplotlib.

Enables advanced data processing with Redis modules including RedisJSON, RediSearch, and RedisTimeSeries for structured data and analytics.

Enables in-context learning through example-based prompting techniques for AI models.

Enables customization of large language models through fine-tuning and prompt-tuning methodologies.

Expert guidance for developing and deploying enterprise AI applications using AWS Bedrock and SageMaker services with industry best practices.

Provides frameworks for testing, measuring, and comparing AI prompts using metrics and A/B testing to enhance model performance.

Enables structured agentic LLM workflows through reusable reasoning and acting patterns for complex task automation.

Designs AI agents using tool-use prompting patterns to enhance functionality and interaction with external systems.

Enables AI systems to store and recall information from previous interactions, maintaining context across user sessions.

Enables advanced PostgreSQL query techniques including Common Table Expressions, window functions, and recursive queries for complex data analysis.

Enables structured step-by-step reasoning for complex problem-solving in AI models, improving logical deduction and solution accuracy.

Teaches foundational PostgreSQL SQL concepts including data types, table structures, constraints, and schema design for effective database management.

Specializes in deploying, fine-tuning, optimizing, and operating OCI Generative AI Dedicated AI Clusters for production AI applications.

Enables efficient querying, indexing, and transformation of JSONB data within PostgreSQL databases.

Provides reusable prompt templates for optimizing AI model interactions across common tasks and use cases.

Builds scalable ETL pipelines, data warehouses, and streaming architectures using Apache Spark, Airflow, and Kafka for data processing and analytics infrastructure.

Optimizes PostgreSQL database performance through query analysis, indexing strategies, and query tuning.