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 This repository is presented as workflows using, primarily, interactive python notebooks `.ipynb`.  Why?  These are easy to review, share, and move.  They contain elements for both code and narrative. The narrative can be written with plain text, Markdown and/or HTML which makes providing visual explanations easy.  This reinforces the goal of this repository: information that is easily accessible, portable, and great for starting points in your own work.
 
-In notebooks, execution is driven from the locally attached compute.  In this repository that means the Python code is currently running in the notebooks compute.  The code in this repository heavily leans on orchestrating services in GCP rather than doing data compute in the local environment to the notebook.  That means these notebooks are designed to run on minimal machine sizes, like `n1-standard2` even.  The heavy work of training and serving is done on Vertex AI, BigQuery, and other Google Cloud services.  You will even find notebooks that author code, and then deploy the code in services like Vertex AI Custon Training and Vertex AI Pipelines.  
+In notebooks, execution is driven from the locally attached compute.  In this repository that means the Python code is currently running in the notebooks compute.  The code in this repository heavily leans on orchestrating services in GCP rather than doing data compute in the local environment to the notebook.  That means these notebooks are designed to run on minimal machine sizes, like `n1-standard2` even.  The heavy work of training and serving is done on Vertex AI, BigQuery, and other Google Cloud services.  You will even find notebooks that author code, and then deploy the code in services like Vertex AI Custom Training and Vertex AI Pipelines.  
 
 There are sections that use other languages, like R, as well as creating files that are external to the notebooks: `dockerfile`, `.py` scripts and modules, etc.