Hey, I'm Saurabh
Data engineer obsessed with causal inference. I write about MMM pipelines, geo-experiments, and the infrastructure that helps marketing teams prove what actually works.
The plumbing behind marketing measurement - from a data engineer's perspective.
Building Marketing Mix Models that refresh daily. Data collection, feature engineering, model deployment.
Incrementality testing when A/B tests aren't possible. Synthetic control, holdout regions, lift measurement.
The systems behind testing at scale. Assignment engines, statistical engines, decision frameworks.
I'm working through a comprehensive study plan covering transformers, diffusion models, and LLMs. Get weekly progress updates, paper summaries, and code implementations.
View Full RoadmapNotes from the transition.
Most ML models find patterns in data but can't answer 'why'. I explain why understanding causality matters for building intelligent systems, especially in robotics and reinforcement learning.
Read Article →RAG isn't magic - it's Extract, Transform, Load with vectors. I break down how your existing pipeline skills map directly to building production AI systems.
Read Article →As a data engineer transitioning into AI, I share why this path makes sense, what advantages we have, and my learning roadmap.
Read Article →Learn how to add observability to LLM applications using OpenTelemetry and data engineering practices.
Read Article →Compare ChromaDB, Pinecone, Weaviate, and pgvector to find the right vector database for your RAG application.
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