How I monitor 13 production AI systems: agent traces, data quality gates, lineage tracking, and cost monitoring
Real-time agent monitoring with trace visualization, eval scoring, latency tracking, and cost monitoring. Ragas faithfulness 0.97 (real eval, OpenAI judge, 18 pairs). 14-tool MCP orchestration. $0.02 cost per request.
51 data quality rules across churn pipeline (customers, transactions, sessions, support). Covers schema validation, null checks, range validation, uniqueness, domain checks, and statistical anomalies. 100% pass rate on real CSV data.
Column-level lineage tracking from raw data → features → model. Visualizes DAG, transformation logic, and test coverage. Shows 12 models, 47 tests passed, and 100% test coverage for production ML pipelines.
Built pipelines on AWS, Azure, and GCP. Everything's Terraform-managed and runs serverless for ~$1-2/month.
Production-grade data pipeline on GCP with BigQuery data warehouse and Cloud Run deployment. Part of multi-cloud infrastructure (3 GCP services). Serverless architecture with $1-2/month runtime costs. Real-time data processing with scalable cloud architecture.
Production-grade data engineering infrastructure on AWS. Built ETL pipelines with Glue, streaming with Kinesis, data quality with Great Expectations, and orchestration with Airflow. Terraform-managed infrastructure: EC2, RDS, S3, Lambda, Athena, CloudWatch. 63 Terraform modules across 10 AWS services.
Geospatial network analysis platform mapping Silicon Beach tech ecosystem. Visualizes company relationships, commute patterns, and network connections through AI-powered Snowflake Cortex intelligence. Built with Snowflake, Streamlit, and Folium.
The messy stuff: point-in-time correctness, training-serving parity, streaming ETL, data quality gates. 51 real DQ rules, 100% pass rate. Sub-ms in-memory fraud scoring.
Azure-native ETL pipeline with Blob Storage, Azure Functions, Cosmos DB, orchestrated by Azure Data Factory + dbt. Implements streaming ETL with data quality validation. Part of multi-cloud infrastructure (5 Azure services). $0/month Azure Free Tier.
Ensemble ML pipeline with time-aware validation. Production Feast feature store: 3 feature views, AWS DynamoDB online serving, offline/online parity tests. Complete ML platform with RFM analysis and data quality gates across the full pipeline.
Streaming feature engine for real-time fraud detection. Sub-millisecond in-memory scoring. Streaming ETL with Kinesis, data quality gates, and drift monitoring. 51 validation rules validated across pipeline datasets.
Built agents that don't break at 3 AM. RAG pipelines, 14-tool MCP orchestration, Ragas faithfulness 0.97 (real eval). Runs serverless for $1-2/month.
Self-healing serverless GenAI platform powered by AWS Bedrock. RAG pipeline with semantic search (Titan Embeddings), memory persistence, and cost controls ($1-2/month runtime). Part of multi-agent architecture with drift detection and auto-rollback.
14-tool MCP server for multi-agent job intelligence. Multi-agent architecture with RAG pipelines, semantic search (DynamoDB vector store), and memory persistence (ChromaDB). Ragas faithfulness 0.97 on RAG eval (real run, OpenAI judge).
Comprehensive multimodal AI application combining text generation, image creation, audio processing, and creative pipelines. Uses Gemini, DALL-E, Whisper, and TTS models for creative content generation. Showcases IBM 'Build Multimodal Generative AI Applications' certification.
SQL generation with RAG and semantic search. Natural language to SQL translation powered by ChromaDB vector database and LangChain agents. Part of multi-agent architecture demonstrating memory persistence and tool orchestration.
Ex-VC/PE → AI builder. Built production tools for legal (VC term sheets), finance (cap table modeling), marketing (campaign analytics), and enterprise BI. Business context = better code.
AI-powered VC negotiation simulator inspired by Silicon Valley. Interactive legal tool teaching term-sheet dynamics through 19+ episode scenarios. See how VC Win, Founder Win, and Win-Win outcomes change based on deal structure.
PE/VC financial modeling platform with VC cap table analysis (Seed → Series B), LP/GP fund economics, and Python-powered Excel automation using openpyxl.
Professional marketing analytics dashboard with real-time KPIs, campaign performance tracking, and cross-platform attribution insights. Built with modern data visualization techniques for marketing professionals.