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+# Central Tenets
+
+* deploying should be as easy as pushing a button
+* reverting should also be easy as pushing a button
+* bad deployment processes impede development speed and innovation 
+    * they create a negative feedback loop of anxiety and fewer pushes
+* good deployment proesses foster development and innovation
+* code isn't useful until it's being used
+
+## Hypothetical
+
+* you have a new feature or a bug fix that's in a rush
+* deploy: what's the worst that could happen?  Disaster.
+
+## Do you have more than one way to 
+
+* deploy code across teams?
+* deploy code amongst a team?
+* deploy a single app?
+
+* having a single process is best, especially in the latter two bullets
+
+## Our old deployment setup
+
+* Debian packaging -- process wasn't maintained, people started wanting a new way to deploy code
+* SCP un-versioned tarballs -- miserable failure
+* Chef installing packages -- worked for a little while, but Chef deployed every 30 mins whether you liked it or not
+* pip installations -- today's talk
+
+## Choose one deployment method and stick with it
+
+### Python-based packaging
+
+* Python & Setuptools to build packages
+* PEP 440 versioned packages
+* pip to install Python packages
+* Jenkins for CI and package building
+* Chef for initial deploy and service discovery
+* Fabric for gluing the pieces together
+
+### How does it work?
+
+* Github used internally
+* Jenkins job runs tests, builds a package, uploads it to an internal PyPI server
+* Project-specific Jenkins job triggers generic deployment job
+* Generic deployment job queries Chef server via Fabric
+* SSH into each node and upgrade the Python package, pip install 'u
+* job restarts service which loads new code: kill -HUP <pid>, test the new code
+* control flow returns to the project-specific job
+* notify monitoring that a deploy happened, include v. and timestamp (HipChat, New Relic)
+* rinse and repeat for all nodes
+
+## Key takeaways
+
+* choose one deployment strategy and iterate on it -- team-wide or org-wide
+* code can be deployed in parallel
+* services should be bounced sequentially
+* centralized deployment job makes for easy rollbacks
+* other codebases can use similar deployment setup
+
+## Shortcomings
+
+* fixing old code deployemnt is very painful
+* strongly coupled with Chef for service discovery- not great with edge cases
+* Jenkins not the greatest continuous deployment server 
+* initial hacky usage of Fabric in early stages
+
+# New vocabulary for me
+
+* "internal PyPi repo"
+
+