Uploaded August 2026 | Updated September 2026, 1 week ago
In this video, you will learn how to use the System Health Overview widget in IBM Sterling Control Center to monitor platform performance and health. We'll explore the metrics service, understand health statuses such as Optimal, Caution, and Poor, configure metric thresholds, analyze historical performance data from CSV files and the database, and review key metrics that can help identify and troubleshoot performance issues.
Learn more about IBM Sterling Control Center → ibm.com/products/control-center
00:00 - Introduction
00:48 - System Health Overview Basics
03:39 - Metrics Collection and Storage
05:07 - Configuring Health Metrics
09:22 - Historical Performance Analysis
14:02 - Troubleshooting with Metrics
20:12 - Key Performance Recommendations
21:39 - Conclusion
#IBM #Support #SterlingControlCenter #Software
In this video, you will learn how to use the System Health Overview widget in IBM Sterling Control Center to monitor platform performance and health. We'll explore the metrics service, understand health statuses such as Optimal, Caution, and Poor, configure metric thresholds, analyze historical performance data from CSV files and the database, and review key metrics that can help identify and troubleshoot performance issues.
Learn more about IBM Sterling Control Center → ibm.com/products/control-center
00:00 - Introduction
00:48 - System Health Overview Basics
03:39 - Metrics Collection and Storage
05:07 - Configuring Health Metrics
09:22 - Historical Performance Analysis
14:02 - Troubleshooting with Metrics
20:12 - Key Performance Recommendations
21:39 - Conclusion
#IBM #Support #SterlingControlCenter #Software


![How AI Is Transforming the US Open Fan Experience | USTA
In this episode of Beyond the Blueprints, Chad Jennings, Global Head of Customer Voice and Product Experience at IBM, sits down with Brian Ryerson, Senior Director of Digital Strategy at the USTA, and Rahul Agarwal, Principal Data Scientist at IBM Consulting, to explore how IBM and the USTA are using AI, real-time data, and predictive analytics to transform the digital fan experience at the US Open.
With as many as 17 matches happening simultaneously, tennis fans need more than a scoreboard to understand where the action is. Brian and Rahul share how IBM SlamTracker and Live Likelihood to Win analyze historical and point-by-point match data to help fans understand momentum shifts, identify compelling matches, and follow the action as it unfolds.
The conversation explores the technology behind the experience, from machine learning and stochastic simulations to watsonx.data, Watson Machine Learning, Red Hat OpenShift, and Instana. Youll also hear how the teams designed an architecture capable of processing live match data and delivering predictions to millions of fans around the world with low latency and high availability—and how more than three decades of collaboration between IBM and the USTA continue to push the US Open digital experience forward.
Topics covered:
[00:00] – Introduction
[00:48] – Meet the USTA and the US Open
[01:34] – How IBM Consulting Partners with the USTA
[02:26] – IBM SlamTracker & Live Likelihood to Win
[04:44] – Designing the Experience Around Tennis Fans
[06:05] – The AI and Machine Learning Models Behind the Predictions
[08:19] – Building the Data and AI Architecture
[11:11] – Processing Point-by-Point Match Data in Real Time
[12:22] – Delivering Predictions to Fans Around the World
[13:03] – Scaling with Red Hat OpenShift and Monitoring with Instana
[14:28] – How IBM Consulting and the USTA Work Together
[15:54] – More Than 30 Years of IBM and USTA Partnership
[16:20] – Lessons Learned from Building AI-Powered Fan Experiences
[18:00] – Fan Engagement and Real-World Impact
[20:09] – Whats Next for AI-Powered Tennis Storytelling
[21:41] – Improving Explainability and Transparency
[22:52] – Closing Thoughts How AI Is Transforming the US Open Fan Experience | USTA](https://i.ytimg.com/vi/QNs-0w1E3cY/mqdefault.jpg)







