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adding prometheus resource
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content/pages/02-development-environments/06-jupyter-notebook.markdown

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@@ -163,6 +163,12 @@ like advanced interactive visualizations.
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uses a freely-available Bitcoin API as a source data set
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for a data analysis and visualization project in Jupyter.
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* [nbdev: use Jupyter Notebooks for everything](https://www.fast.ai/2019/12/02/nbdev/)
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shows how to use the [nbdev](https://nbdev.fast.ai/) tool to create a literate
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programming environment within a Jupyter Notebook so that you can do
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all of your debugging and refactoring there rather than switching between
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a more traditional IDE and Jupyter.
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* [Running Jupyter Notebooks on GPU on AWS: a starter guide](https://blog.keras.io/running-jupyter-notebooks-on-gpu-on-aws-a-starter-guide.html)
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explains how to run notebooks on Amazon Web Services using a
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graphics-processing unit (video card), which for some machine learning

content/pages/06-devops/02-prometheus.markdown

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* [A gentle introduction to the wonderful world of metrics](https://tech.showmax.com/2019/10/prometheus-introduction/)
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has a quick summary that compares Prometheus with Nagios, then digs
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into the logging format and what you can visualize with this tool.
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* [From Graphite to Prometheus](https://engineering.nanit.com/from-graphite-to-prometheus-things-ive-learned-e1d1e4b97fc)
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explains some of the differences between using a StatsD / Graphite
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monitoring stack and Prometheus, such as how Prometheus scrapes data
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instead of the applications pushing data to a metrics aggregator,
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and the query languages for each tool.

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