...................................................................................... 41 12 Metrics ................................................................................................ manager and/or systems engineer) who coordinates and integrates programmatics (e.g., schedules, metrics) and deliverables (e.g., code)?  Is there a clear owner of the program (or broader enterprise) review/approve?  Does the government have access or insight into the development environment (code, metrics)?  At the end of a sprint, does the team reflect on what worked and how to improve in future

environments layer a. Events, logs, & metrics b. Common service to centralize, rotate, and delete 2. Event router responsible for storing our events and metrics a. Enables visualization, trending, alerting good things b. Enable threshold-based alerting & health checks iii. Transform logs/events into metrics to enable statistical analysis iv. Adrian Cockcraft – “Monitoring is so important that our monitoring prevent them in the future ii. Use for fact-based problem-solving e. ENABLE CREATION OF PRODUCTION METRICS AS PART OF DAILY WORK i. Create infrastructure to make it easy for Dev or Ops to create telemetry

browser Metrics, metrics everywhere (but where the heck do you start?) by Tammy Everts & Cliff Crocker (SOASTA) @tameverts @cliffcrocker http://www.slideshare.net/CloudTest/metrics-metrics-everywher

Guidance 1. Pull all factual evidence (chat logs, etc.) to help build the timeline; any specific metrics observed, investigative paths taken, results, and other resolutions considered 2. Don’t allow fear deployment pipeline demonstrates that changes can be low risk and pre-approved. ii. Use previous metrics to demonstrate high success rates and low MTTR iii. Link and provide traceability from planning

by a model. 02 Summarization Evaluating how a model condenses information, using 
 agreed-upon-metrics for accuracy, relevance, and coherence. 03 Human trainers Comparing AI results to responses from the output of a model against 
 a benchmark—is it more accurate? More compliant? Safer? Your key metrics will depend on what matters most for each 
 use case. 8 AI in the EnterpriseLesson 2 Embed AI

powerful, accessible, and economically viable. OpenAI’s ChatGPT – based on user / usage / monetization metrics – is history’s biggest ‘overnight’ success (nine years post-founding). AI usage is surging among moving averages… AI DJ, we’re seeing amazing results, not just on quantitative metrics, but also on quality metrics, how people feel about Spotify, what they say they love about Spotify. - Spotify

how Agile can underpin EVM progress in support of tracking program cost and schedule status and metrics • The scenario is a generic SW program implementing an Agile development process • The scenario

processes, or review processes 2. Places where significant rework is generated or received. iii. Key metrics in value stream 1. Lead Time 2. Process time 3. Percent Complete & Accurate (%C/A) c. CREATE

Expands upon Canary. Links production monitoring with release process to automatically roll back when metrics deviations, outside of expected ranges, are detected i. APPLICATION-BASED PATTERNS TO ENABLE SAFER

Low-level synchronization primitives . . . . . . . . . . . . . . . . . . . . . . . . . . 1172 50.4 Task metrics (Experimental) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1173 51 Scoped Values source 50.4 Task metrics (Experimental) Base.Experimental.task_metrics – Function. Base.Experimental.task_metrics(::Bool) Enable or disable the collection of per-task metrics. A Task created when Experimental.task_metrics(true) is in effect will have Base.Experimental.task_running_time_ns and Base.Experimental.task_wall_time_ns timing information available. Note Task metrics can be enabled at