SQL Server and the Vector Revolution - how LLMs see your data
Embeddings, vector search and the bridge between traditional data platforms and modern AI.
Speaking
Talks for teams building real systems, not only demos
Session themes revolve around MLOps, Azure, Databricks, AI, data observability and the operational reality of shipping data and ML platforms.
The emphasis stays practical: lessons from production systems, architectural trade-offs and sessions that help engineering teams move from abstract interest to workable delivery patterns.
Sessions
Sessions and conference appearances
A mix of MLOps, data observability, Databricks orchestration and vector-native AI topics grounded in real delivery work.
BigQuery, Vertex AI or Databricks? Don’t choose - combine them!
A reference architecture for building one AI platform across BigQuery, Databricks and Vertex AI without creating new silos.
Databricks Overwatch - the Dashboard that sees everything
Monitoring Databricks environments through Overwatch, from installation to dashboards and production lessons learned.
MLOps on Azure - war stories and lessons learned
Operationalizing ML in regulated and edge-heavy environments through real production lessons from Azure-based systems.
Efficient data streaming solutions at Scale with Azure
Streaming architectures and Azure-native delivery patterns for reliable data movement and processing at scale.
A Hitchhiker's guide to AI/ML
A practical starting map for entering AI/ML without getting lost in tooling, hype and bad early decisions.
Understand your data landscape with Observability platform
Designing data observability in Azure so teams know what data exists, how it moves and whether it can be trusted.
Formats
Ways of working with engineering teams
Conference sessions
Architecture talks, technical sessions and practical case studies for engineering-focused events.
Hands-on workshops
Structured workshops on MLOps, Databricks, MLflow, Azure delivery pipelines and production patterns.
Internal enablement
Talks adapted for engineering organizations that need a sharper view of platform and operating-model choices.