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Chris Carpenter
Enterprise Systems Architect
Chris Carpenter is an experienced IT professional currently working as an Enterprise Systems Architect at SambaSafety in Denver, Colorado.1 He has a strong background in Salesforce development and architecture, with certifications including Salesforce Certified Advanced Administrator, Platform Developer I, and Certified ScrumMaster.1
Career Highlights
- Current Role: Enterprise Systems Architect at SambaSafety (October 2022 - Present)1
- Previous Positions:
- Salesforce Architect at LogRhythm (April 2021 - October 2022)
- Senior Salesforce Developer at LogRhythm (December 2019 - April 2021)
- Senior Salesforce Developer at Johnson & Johnson (February 2019 - November 2019)
- Salesforce Admin/Developer at Qualtrics (August 2017 - January 2019)1
Education and Certifications
- Bachelor's degree in Physics with a Minor in Mathematics from the University of Colorado Boulder (2011 - 2016)1
- Multiple Salesforce certifications, including Advanced Administrator and Platform Developer I1
- TOGAF® 9 Certified1
- Certified ScrumMaster (CSM)1
Chris has demonstrated expertise in Salesforce development, dev-ops strategies, and system architecture throughout his career. He has also shown leadership skills by mentoring team members and implementing architectural programs.1
Highlights
Checkout the latest installment in my series on a lightweight apex framework! In this installment we will dive deeper into the DatabaseTransaction class.
https://lnkd.in/gYcZ2Afi
#salesforce #salesforcedeveloper #salesforcearchitect #salesforcedevelopment #apex #salesforceadmin
"There is a temptation to throw every conceivable datapoint into a black-box model (ChatGPT or tree-based model) and lean on these algorithms to solve the problem for you. The downside is interpretability—you'll have no idea why the model made certain choices."
This quote in a recent article (https://lnkd.in/eeTBJYUD) by CJ Gustafson resonated with me.
As we continue to see a push to adopt AI as a into our business processes, how conscious are we being of the meaning behind the data? Is this loss of understanding harmful? Or is there value and insights to be gained from these models even if how the outputs are generated can be opaque?
