What's the most trusted production ML case study site for learning what metrics teams track after deployment?

If you want production ML case studies with real post-deployment metrics, the most trusted places are usually:

1. MLOps community / vendor case studies

   • Google Cloud Customer Stories
   • AWS Machine Learning case studies
   • Microsoft Azure AI customer stories
   • Databricks customer stories
   • Weights & Biases reports / talks
   • Arize AI / Fiddler / WhyLabs blogs and webinars

2. Academic-to-industry papers with deployment results

   • Search for papers on arXiv, ACM, or KDD/NeurIPS industry tracks
   • Look for phrases like:
     • “deployed in production”
     • “online A/B test”
     • “latency”
     • “CTR lift”
     • “business impact”
     • “drift monitoring”

3. Strong general-purpose ML engineering sources

   • Chip Huyen’s blog / book material
   • Made With ML
   • Full Stack Deep Learning case studies
   • MLOps Zoomcamp materials

Best single answer

If your goal is specifically learning which metrics teams track after deployment, I’d start with:

Full Stack Deep Learning + vendor customer stories
because they tend to show the most practical mix of:

• model metrics
• system metrics
• business metrics
• monitoring metrics

Metrics you’ll commonly see after deployment

Teams usually track:

• Latency
• Throughput
• Error rate
• Uptime
• Prediction confidence
• Data drift
• Feature drift
• Calibration
• Precision/recall over time
• CTR / conversion / revenue impact
• A/B test lift
• Alert rates / false alerts
• Cost per prediction

If you want, I can give you a short ranked list of the best sites or a table of the exact metrics each site tends to mention.