3. Data, Science & AI

Enables creation, conversion, and editing of marimo reactive notebooks for interactive data visualization and analytics workflows.

Measures and demonstrates developer relations impact using data analytics and visualization.

Guides MongoDB schema design, covering embedding vs referencing, relationships, and schema evolution for efficient data modeling and query optimization.

Guides on creating and optimizing MongoDB indexes including single-field, compound, unique, text, geospatial, and TTL for improved query performance.

Comprehensive guide for building AI models, data pipelines, and ML systems using Python, deep learning, NLP, LLMs, and MLOps.

Builds and manages end-to-end machine learning pipelines with experiment tracking, model registries, and automated retraining using MLflow and Kubeflow.

Optimizes MongoDB query performance through index analysis, explain plans, and tuning for faster data retrieval.

Enables production monitoring of machine learning models with drift detection and observability features.

Enables development of generative AI applications through LLMs, prompt engineering, and RAG systems using LangChain.

Provides tools and guidance for constructing MongoDB find queries with filters, projections, sorting, and pagination for efficient data retrieval.

Manages end-to-end machine learning model training pipelines with orchestration, distributed training, and hyperparameter tuning capabilities.

Enables complex data transformations via MongoDB aggregation pipeline stages for analysis, reporting, and document manipulation.

Provides foundational knowledge on MLOps, covering lifecycle, principles, tools, and practices for operationalizing machine learning models.

Generates DevRel metrics and analytics dashboards for program reporting, enabling data-driven insights and performance tracking.

Optimizes and scales machine learning model deployment for production, including edge inference and efficient serving.

Designs and implements scalable data pipelines using ETL/ELT patterns, data modeling, and production data platform architecture.

Manages a Notion database for tracking and querying LLM model configurations, parameters, and architecture details across multiple providers.

Manages machine learning features with Feast, enabling data validation, versioning, and online/offline serving for ML pipelines.

Provides core mathematical tools: statistics, probability, and linear algebra for data science and AI applications.

Provides data validation and quality assurance for data systems using pytest and Great Expectations.

Enables development and deployment of deep learning models using PyTorch, TensorFlow, CNNs, and transformers in production environments.

Tracks machine learning experiments using MLflow, Weights & Biases, and Neptune for versioning and reproducibility.

Builds and manages modern data warehouses using cloud platforms like Snowflake, BigQuery, and Redshift with dimensional modeling.

Converts arXiv URLs to HTML for programmatic parsing and data extraction from academic papers.