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. Shows 93% task success rate, ~1% hallucination rate (Ragas faithfulness 0.92), 149ms p95 latency, and $0.02 cost per request across 100+ production workflows.
47 validation rules preventing bad data from reaching production. Covers schema validation, null checks, range validation, uniqueness, pattern matching, and statistical anomalies. Achieved 95.7% success rate with 12→0 incidents/month.
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. Hit 98.8% AUC, 149ms p95, went from 12 incidents/month → 0.
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.
Achieved 98.8% AUC using ensemble models (XGBoost, LightGBM, CatBoost) with time-aware validation. Complete ML pipeline with RFM analysis, 47 Great Expectations rules, and training-serving parity. Reduced false positives by 40%.
Optimized API latency to 149ms p95 for real-time fraud detection. Streaming ETL with Kinesis, 47 Great Expectations validation rules, and drift monitoring. Reduced data quality incidents from 12→0/month.
Built agents that don't break at 3 AM. RAG pipelines, 14-tool orchestration, 93% task success, ~1% hallucination. 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.
Orchestrated 14-tool agent achieving 93% task success rate across 100+ production workflows. Multi-agent architecture with RAG pipelines, semantic search (DynamoDB vector store), and memory persistence (ChromaDB). Achieved ~1% hallucination rate using Ragas evaluation.
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.