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

specific tasks within larger systems ◮ Applications: ◮ Consumer electronics (e.g. mobile phones, VR headsets) ◮ Home automation (e.g. thermostats) ◮ Medical equipment (e.g. pacemakers) ◮ Characteristics: Python, on a laptop Dataset Train duration Model size (MB) Small 2m15s 23 Big 7m10s 23 36 / 50Test the Rock-Paper-Scissors model ◮ Using the testing data of the big dataset (124 images per class)

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

core system 6Why use plugin architecture? ● More testable code ○ Interface can be mocked to test core system ○ Plugins can be individually tested ● Simplifies integration ○ Core system has a well 58Kinematics 59Kinematics 60 Plugins built on top of ● TrackIK ○ TRACLabs, Inc. Robotics and Automation ● KDL ○ Kinematics and Dynamic Library ● BioIK ○ Philipp Ruppel as part of his Master Thesis through shared_library class ○ Tutorials ● POCO ○ SharedLibrary class ● Qt Plugins ○ Expand Qt UI application. 66● Plugins have to compiled with the same or compatible compiler as the core system

not enough memory allocation fails accessing null uncontrolled reset Q.E.D 43 Wrong!I needed to test my assumptions… 4445 Memory Profiler I couldn’t use standard memory profilers as the Linux kernel day) per unitUse the data you already have 156 Be proactive!Testing 157TESTING 158 ⊡ Rewrote the test paper from manual tests to automatic tests. ⊡ Implemented the automated tests. □ Two pipelines pipelines – long and short tests. □ 24/7 - to stabilize the tests. E2E tests:TESTING 159 ⊡ Rewrote the test paper from manual tests to automatic tests. ⊡ Implemented the automated tests. □ Two pipelines

Flexible Timing (5-10 milliseconds) ❖ ! A guaranteed response time ❖ Milliseconds ❖ Home automation Hard/Soft Real Time RequirementsWhat level of time precision does our system require?What level logic layer from the hardware layer Application Layer Drivers handling LayerHow are we going to test it?Probably, we’re not…Logic GetNextTrafficLightLogic SetTrafficLightLogic Hardware SetTrafficLightA scenarios that would be difficult to test in real lifeInterfaces simulator Load simulator The simulator allows us to replicate scenarios that would be difficult to test in real lifeSimulator #1Communication

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