USA-Based LLM: Total Current Users Outside North America Note: LLM data is for monthly active mobile app users. App not available in select countries, including China and Russia, as of 5/25. Source: included in East Asia figures. Data for standalone app only. Source: Sensor Tower (5/25) 5/23 4/25 Mobile App Monthly Active Users, MM Details on Page 315 AI & Work Evolution = Real + Rapid 8 USA → More Note: PC units as of 2000. Desktop internet users as of 2005, installed base as of 2010. Mobile internet units are the installed based of smartphones & tablets in 2020. Cloud & data center capex

fine-tune your models 13 Get AI in the hands of experts 16 Unblock your developers 18 Set bold automation goals 21 Conclusion 22 More resources 24 2 AI in the EnterpriseA new way 
 to work As an AI development lifecycle can multiply 
 AI dividends. 07 Set bold 
 automation goals Most processes involve a lot of rote work, ripe for automation. Aim high. Let’s drill down into each of these, with customer dramatically reduced search time; and advisors spend more time on client relationships, thanks to task automation and faster insights. The feedback from advisors has been overwhelmingly positive. They’re more

Netflix twice in 18 month span. He wasn’t fired, he had also helped move their operations and automation forward by “light-years” and had performed huge number of production deployments. g. INJECT PRODUCTION CHAT BOTS TO AUTOMATE AND CAPTURE ORGANIZATIONAL KNOWLEDGE i. ChatOps pioneered at GitHub – put automation tools (Hubot) into the middle of their chatrooms 1. Everyone saw everything that was happening PRACTICE i. Ensure automated tests demonstrate use and behavior of libraries and components ii. Test suite becomes the living documentation of the system specification and represent working examples

optimization.  Environment homogenization and assimilation – no snowflakes  Deployment methodologies, automation, monitoring, and management tested continuously. Steve Barr steve.barr@csgi.com @srbarr1 Overall technical learning.  How we need to improve  Sharing ideas, code, Community of Practice, etc.  Test Driven Infrastructure  Blue – green deployments  Combining DevOps Scrum – planning, standups Scrum, Craig Larman  Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation, Jez Humble and David Farley  The Phoenix Project: A Novel About IT, DevOps, and Helping

Means Eliminating IT Operations, or “NoOps” f. Myth—DevOps is Just “Infrastructure as Code” or Automation: g. Myth—DevOps is Only for Open Source Software: 2. Foreword xix 3. Imagine a World Where constraint usually follows this progression: a. Environment creation: b. Code deployment: c. Test setup and run: d. Overly tight architecture: iv. ELIMINATE HARDSHIPS AND WASTE IN THE VALUE STREAM Dev teams to spend more time building functionality, as opposed to created infrastructure ii. Automation – across the board iii. Create known, good environments that are ready for production b. EMBED

Clean Code - Book Overview 2 Kyle Baardson SAFe: Scaled Agile Framework 3 Brandon McAllister Automation 2 Eric Collins The Servant: Agile Book Club 1 Ron Horner Kanban for the People! 2 Steve Miller Ballou Notes from Velocity 1 Chris Krull Technical Humility 1 Jason Wohlgemuth Agile Front-end Automation 4 Nik Kalantjakos "Coaching Agile Teams" - Book Club 0 Ray Page CA Clarity PPM software for Agile Mike Ballou Pomodoro Technique 4 Mark Staroska Story Points 4 Jason Wohlgemuth Agile Front-end Automation 4 Kyle Baardson SAFe: Scaled Agile Framework 3 Josh Sagucio Collaborative Work Environments 3

Clean Code - Book Overview Kyle Baardson SAFe': Scaled Agile Framework Brandon MCcAllister Automation Sep 2014 Eric Collins The Servant: Agile Book Club Ron Horner Notes from Velocity Chris Krull Technical Humility Jul 2015 Jason Wohlgemuth Agile Front-end Automation Nik Kalantjakos "Coaching Agile Teams'" - Book Club Ray Page CA Clarity PPM software for Agile

generate optimized program for new operator workloads and hardware Hardware FrameworksWhy Automation is the Future Clear winner on emerging models in product Competitive on benchmarking type model enables other optimizations: fusion, layout, parallelization Portable performance across devicesWhy Automation is the Future 1 1 1 1 0.76 0.83 1.16 1.44 Large MatMul BatchConv Small MatMul BatchMatMul

enterprises, seamlessly integrating with modern data stacks to fuel predictive analytics and intelligent automation. It is key for businesses needing a centralized solution for real-time, AI-driven operations, rather

from accessing the results, as in this example from Motor: import motor db = motor.MotorClient().test @gen.coroutine def loop_example(collection): cursor = db.collection.find() while (yield cursor.fetch_next): the client closes the connection (but see that method’s docstring for caveats). 6.1.6 Templates and UI Tornado includes a simple, fast, and flexible templating language. This section describes that language handler.xsrf_form_html • reverse_url: alias for Application.reverse_url • All entries from the ui_methods and ui_modules Application settings • Any keyword arguments passed to render or render_string When