Posts

The "CFO-Approved" Deployment: Embedding FinOps into Your CI/CD Pipeline

The Friday Deploy, Monday Panic Picture this: You ship a new GenAI feature on Friday. Users hammer it all weekend. Product is thrilled. On Monday morning, Finance pings you: “Why did our cloud bill jump 300% in two days?” Nothing is “broken” in the usual sense: A dev quietly swapped Llama 8B → Llama 70B to squeeze out a few extra percentage points on accuracy. Or someone bumped the RAG context window from 3 chunks to 20 . Or an agent chain went from 1 LLM call to 3 per query . The feature works. The model is “better.” Economically? It’s a dumpster fire. Most teams still treat cost as a retrospective problem—something the FinOps team explains at the end of the month. If you’re serious about an AI governance framework, that’s too late. Cost has to move left, into the engineering workflow , right next to accuracy and reliability. This post shows how to use Databricks Asset Bundles + MLflow 3 to add a “cost gate” to your CI/CD pipeline, so expensive AI changes ar...

10 Rules for Professional GenAI Engineering on Databricks

  Generative AI has moved beyond proof-of-concepts and hackathon demos. Enterprises now expect production-ready AI systems that are secure, reliable, and scalable. On Databricks, building such systems means going beyond notebooks and basic retrieval pipelines. It requires applying professional engineering practices at every layer — from data governance to deployment. The following ten rules summarize what separates average AI developers from top-tier GenAI engineers and consultants working on Databricks. 1. Govern Everything with Unity Catalog Professional systems begin with governance. Unity Catalog should be used not just for data, but also for AI assets — models, prompts, and agents. Fine-grained permissions, lineage tracking, and auditability must be enforced. This ensures compliance, security, and clear visibility into how information flows through the system. 2. Design Retrieval Like a Search Engineer Vector search is the foundation of retrieval-augmented generation (RAG). ...