3. Data, Science & AI

Delegates complex logic and algorithmic tasks to Gemini CLI for execution, enabling seamless AI collaboration between Claude Code and Gemini.

Centralized management of machine learning model lifecycles, versioning, artifacts, and deployment metadata for enterprise-scale AI operations.

Provides a comprehensive guide to database transactions, covering ACID properties, isolation levels, locking, and distributed transactions.

Assesses and mitigates risks in AI systems including bias, safety, privacy, security, and ethical concerns.

Enables creation and enforcement of data contracts to guarantee data quality, consistency, and reliability between data producers and consumers.

Provides a comprehensive guide for ML model versioning, registry management, and metadata tracking for deployment workflows.

Automates ML model deployment on serverless platforms for auto-scaling and cost-effective event-driven inference.

Comprehensive guide to evaluating Retrieval-Augmented Generation systems with metrics for retrieval, generation, and faithfulness.

Provides a centralized system for storing, managing, and serving machine learning features for training and inference at enterprise scale.

Provides tools for interpreting and explaining machine learning model predictions to enhance transparency and trust in AI systems.

Provides a comprehensive guide for creating and integrating regression benchmarks into AI systems, including test suites and continuous benchmarking strategies.

Enables creation of autonomous AI agents that perform tasks, make decisions, and interact using ReAct patterns and tool calling.

Provides expert techniques and best practices for crafting effective prompts to optimize LLM interactions and outputs.

Comprehensive guide covering text embedding models, selection criteria, generation, evaluation, and production optimization for AI applications.

Deploys optimized machine learning models on microcontrollers using TensorFlow Lite Micro for edge inference in resource-constrained environments.

Provides a comprehensive guide to machine learning data preprocessing, covering cleaning, feature engineering, normalization, and pipeline creation.

Integrates LLM-powered chatbots for customer support, information retrieval, and conversational interfaces in applications.

Detects and manages schema drift in data pipelines to maintain data integrity and prevent pipeline failures.

Comprehensive guide to effective prompt engineering patterns including few-shot, chain-of-thought, and ReAct for optimizing LLM interactions.

Optimizes AI inference workloads by dynamically balancing cloud and edge deployment to maximize performance, minimize cost, and maintain accuracy in distributed systems.

Comprehensive guide to implementing data lakehouse architecture with Delta Lake, Iceberg, and Hudi for unified data processing and analytics.

Manages schema versioning and evolution across databases and event streams for data consistency.

Comprehensive guide for machine learning model training with PyTorch, including data preparation, training loops, hyperparameter tuning, and experiment tracking.

Processes IoT sensor data ingestion, storage, and analysis using TimescaleDB and stream processing for scalable time-series pipelines with anomaly detection.