Data & ML Engineering

Streaming for ML (Kafka/Flink) Architectures

Design streaming data platforms for real-time ML: Kafka, Flink, and feature computation pipelines.

Feature Store Design

Design and operate feature stores for consistent online/offline ML feature serving.

Event Schemas & Data Contracts in Practice

Design event schemas and data contracts for reliable data pipelines in ML systems.

Data Quality & Observability

Monitor and ensure data quality with automated testing, observability, and SLA management.

Lakehouse Governance & Table Formats

Govern data lakehouses with modern table formats: Iceberg, Delta Lake, and Hudi.

Vector Data Infrastructure in Production

Deploy and operate vector databases and indexes for production ML and RAG systems.

Low-Latency Serving (gRPC, Arrow Flight, Triton)

Build low-latency ML serving systems using gRPC, Arrow Flight, and Triton Inference Server.

Security & Privacy Patterns for Data Platforms

Secure data platforms for ML: encryption, access control, and privacy-preserving data processing.

Data Catalogs & Lineage Systems

Implement data catalogs and lineage tracking for discoverability and governance in ML platforms.

Access Control & Privacy-by-Design

Implement RBAC/ABAC and privacy-by-design principles for compliant ML data platforms.

Experiment Tracking & Reproducibility at Scale

Track ML experiments and ensure reproducibility with MLflow, W&B, and best practices.

Efficient Hyperparameter Optimization

Bayesian optimization, BOHB, and multi-fidelity methods for efficient hyperparameter tuning.