Blog
Notes on applied ML systems—APIs, batch jobs, and retrieval—plus practical data work (reporting, cleaning, workflows) when that is what the problem needs.
For runnable examples, see Portfolio. For scoped work, see Services.
/ Technology, Automation
When to serve a model with an API vs a batch job
Not every model needs a real-time endpoint. Here is how to choose between online inference and scheduled batch scoring without overbuilding.
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/ Technology, Analysis
RAG that holds up: citations, chunking, and grounding
“Chat with your docs” demos are easy; trustworthy answers are not. Here is what actually breaks, and what to enforce so replies stay tied to sources.
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/ Technology, Data Integration
One feature table for training and scoring
If training and production scoring use different column logic, the model’s inputs quietly drift. Here is why one shared feature build beats ad-hoc spreadsheets for serious ML.
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/ Dashboards, KPI
5 Essential KPIs Every Small Business Should Track
Not sure which numbers matter? Here are the five KPIs that give you the clearest view of how your business is really performing.
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/ Dashboards, Sales
How to Build a Sales Dashboard That Actually Gets Used
A dashboard is only useful if people look at it. Here's how to design one that your team will actually use.
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/ Marketing, Reporting
Turning Campaign Data Into Actionable Marketing Reports
Raw campaign data is overwhelming. Here's how to turn it into reports that help you decide what to do next.
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