Learning Launch - Follow the Sun Deploys 

 The Challenge :   Currently , I work as a Release Manager for my organization . We have a collaboration software   that helps drive work forward . In my organization we believe in quick iteration , fast feedback   loops , and responsiveness to customer feedback . To add to this we also have a number of   Engineers who were suffering given that our deploys were not reliable causing frustrations   and   a decline in productivity . . With these values and issues in mind I was given the   challenge of   refining our deployment system towards continuous integration and deployment so that we can   release more frequently but focussing on reliability and safety .    The project team includes the Developer Productivity / Internal Tools teams . The scope of work is   estimated to take about two sprints ( 4 weeks ) to complete .    With our approach being very manual we are very dependent on humans deploying the latest   version of our software which   makes our deploys error prone and slow .    To add to this issue , we have Deploy Commanders in the East Coast that are burdened by   these manual deploys as well . As part of this launch we will focus on understanding our key   stakeholder ’s ( deploy commanders and engineers ) needs   and how we can make deploys more   safe , reliable and fast .       Selection :   After carefully looking at our problem the team has decided to use ​Learning Launch​ tool to   introduce an experiment with our deploy commanders to quickly test our idea / proposal of “ follow   the sun deploys ”        Application :   As part of our first learning launch , we focused on introducing a regular deploy cadence starting   Monday through Friday . We introduced a rotation where we would do about 12 scheduled   deploys . During each deploy , an engineer is designated as the deploy commander in charge of   rolling out the new build to production . Having this regular cadence would introduce the   reliability the engineers found productive and it also gave us the safety we required . As part of   this experiment we included Deploy Commander training as part of our new Engineer   Onboarding .     Secondly , after we saw the success of the deploy rotation as part of the second experiment we   started introducing these shifts in other timezones such as Pune , Melbourne and Dublin .   As we rolled these experiments out we regularly sent out engineering effectiveness surveys to   get   quick feedback on the new deploy process . 

 Insight :   By applying this tool and conducting these experiments we gained insight into the thought that   even though these deploy shifts relieved the concerns of a few engineers , most of them   expressed their desire for complete automation of the deploy process . As anticipated   introducing these shifts across various timezones proved to be extremely beneficial by providing   complete coverage through the 24 hour period and hence we ended up calling this our “ follow   the sun experiment ” .   Having these additional shift also eased the burden on our East Coast Engineers .   We are continuing to include various mechanisms to take . us to our final goal of completely   eliminating Deploy Commanders and having the entire release process automated .       Approach :   Our team is continuing to use this approach to introduce tiny experiments across the various   deploy processes to see where we can eliminate manual intervention . This has allowed us to fail   fast , pivot and gain the quick feedback / insight we require from our stakeholders / customers . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: 
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: 
USERNAME: 

### Task: You are a PII (Personal Identity Information) Detection. Given a essays written by students, you have to identify ONLY the following:
NAME_STUDENT - The full or partial name of a student that is not necessarily the author of the essay. This excludes instructors, authors, and other person names.
EMAIL - A student’s email address.
USERNAME - A student's username on any platform.
ID_NUM - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.
PHONE_NUM - A phone number associated with a student.
URL_PERSONAL - A URL that might be used to identify a student.
STREET_ADDRESS - A full or partial street address that is associated with the student, such as their home address.
### Input: Learning Launch - Follow the Sun Deploys 

 The Challenge :   Currently , I work as a Release Manager for my organization . We have a collaboration software   that helps drive work forward . In my organization we believe in quick iteration , fast feedback   loops , and responsiveness to customer feedback . To add to this we also have a number of   Engineers who were suffering given that our deploys were not reliable causing frustrations   and   a decline in productivity . . With these values and issues in mind I was given the   challenge of   refining our deployment system towards continuous integration and deployment so that we can   release more frequently but focussing on reliability and safety .    The project team includes the Developer Productivity / Internal Tools teams . The scope of work is   estimated to take about two sprints ( 4 weeks ) to complete .    With our approach being very manual we are very dependent on humans deploying the latest   version of our software which   makes our deploys error prone and slow .    To add to this issue , we have Deploy Commanders in the East Coast that are burdened by   these manual deploys as well . As part of this launch we will focus on understanding our key   stakeholder ’s ( deploy commanders and engineers ) needs   and how we can make deploys more   safe , reliable and fast .       Selection :   After carefully looking at our problem the team has decided to use ​Learning Launch​ tool to   introduce an experiment with our deploy commanders to quickly test our idea / proposal of “ follow   the sun deploys ”        Application :   As part of our first learning launch , we focused on introducing a regular deploy cadence starting   Monday through Friday . We introduced a rotation where we would do about 12 scheduled   deploys . During each deploy , an engineer is designated as the deploy commander in charge of   rolling out the new build to production . Having this regular cadence would introduce the   reliability the engineers found productive and it also gave us the safety we required . As part of   this experiment we included Deploy Commander training as part of our new Engineer   Onboarding .     Secondly , after we saw the success of the deploy rotation as part of the second experiment we   started introducing these shifts in other timezones such as Pune , Melbourne and Dublin .   As we rolled these experiments out we regularly sent out engineering effectiveness surveys to   get   quick feedback on the new deploy process . 

 Insight :   By applying this tool and conducting these experiments we gained insight into the thought that   even though these deploy shifts relieved the concerns of a few engineers , most of them   expressed their desire for complete automation of the deploy process . As anticipated   introducing these shifts across various timezones proved to be extremely beneficial by providing   complete coverage through the 24 hour period and hence we ended up calling this our “ follow   the sun experiment ” .   Having these additional shift also eased the burden on our East Coast Engineers .   We are continuing to include various mechanisms to take . us to our final goal of completely   eliminating Deploy Commanders and having the entire release process automated .       Approach :   Our team is continuing to use this approach to introduce tiny experiments across the various   deploy processes to see where we can eliminate manual intervention . This has allowed us to fail   fast , pivot and gain the quick feedback / insight we require from our stakeholders / customers .
### Output: 