From the course: SQL for AI Projects: From Data Exploration to Impact
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Data preparation - SQL Tutorial
From the course: SQL for AI Projects: From Data Exploration to Impact
Data preparation
- [Narrator] Before we can measure anything meaningful, we'll need to transform raw AI interaction data into a format that's structured, aggregated, and ready for analysis. This transformation step helps us filter out noise, create derived metrics, and prepare for experiments that test AI agent performance. We'll be using SQL to reshape and combine our data sets just like we would in any production analytics pipeline. To start, we'll use SQL to perform some foundational transformations on our AI log data. This includes applying where filters to focus on relevant interactions using group by aggregates to calculate metrics by agent visit or experiment variant computing percentile values like the 95th percentile of latency, and using some case when logic to create custom metrics like escalation rates or success flags. Now let's bring everything together. To run a proper analysis of AI agent experiments, we need to join…
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