[DSC Adria 23] Abhisar Bharti Real-Time Monitoring Using Prometheus and Grafana.pdf
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In this talk, I will introduce Portainer, Prometheus and Grafana to do an effective monitoring of an infrastructure. We will also look into the Prometheus configuration in detail with optimization in mind and a small demo of a simple use case. We will cover some basic challenges of using prometheus in a production environment and look for possible steps to overcome those issues.
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[DSC Adria 23] Abhisar Bharti Real-Time Monitoring Using Prometheus and Grafana.pdf
- 1. Real-Time Monitoring Using Prometheus and Grafana Machine Learning Reply | DSC Adria 23 | May 18th Presented by: Abhisar Bharti
- 2. 2 Agenda Machine Learning Reply | DSCAdria 23 | May 18th About Reply & Abhisar Bharti 1 Monitoring and Machine Learning Operations 2 Prometheus and Grafana: Advantages, Limitations, Challenges & Outcomes 3 Summary 4
- 3. 3 Agenda Machine Learning Reply | DSCAdria 23 | May 18th About Reply & Abhisar Bharti 1 Monitoring and Machine Learning Operations 2 3 Summary 4 Prometheus and Grafana: Advantages, Limitations, Challenges & Outcomes
- 4. About Reply & Abhisar Bharti Global presence and growth Reply at a glance - Our growth in revenue and employees Employees Revenue In Mio € State-of-the-art technologies High investments in future technologies and the establishment of leading-edge technologies lead to value creation and long- term customer relationships. Wachstumsfaktoren Top-Talents Hiring top talent and developing employees into accepted leaders within their industry or technology. Start-up Concept Creation of Reply start-ups for new trends and technologies, knowledge building and "survival of the fittest". Croatia Zagreb Brazil São Paulo, Belo Horizonte Berlin, Bremen, Düsseldorf, Frankfurt, Gütersloh, Hamburg, München Germany France & Benelux Paris, Amsterdam, Brüssel, Luxemburg Italy Bari, Mailand Padova, Rom, Turin, Trieste, Verona London, Basingstoke, Chester, Cockpole Green Great Britain Poland & Romania Katowice, Bucharest Belarus Minsk Chicago, Detroit, Seattle USA China Peking Vienna Austria 230 277 330 340 384 440 495 560 632 706 781 884 1036 1182 1250.2 1483.8 1891.1 2006 `07 `08 `09 `10 `11 `12 `13 `14 `15 `16 `17 `18 `19 `20 `21 `22 1925 2272 2686 2994 3149 3422 3725 4253 4689 5245 6015 6456 7606 8157 9059 10579 13467 4 Machine Learning Reply | DSCAdria 23 | May 18th
- 5. 5 Machine Learning Reply - United by the Digitalization About Reply & Abhisar Bharti ARTIFICIAL INTELLIGENCE Intelligent Automation Machine Learning CLOUD PLATFORMS Development & Operations Data CYBER SECURITY Security Operation Center Security Consulting Internet of Things AUTONOMOUS VEHICLES INDUSTRIAL SYSTEMS CONNECTED PRODUCTS ENERGY ECOSYSTEMS HEALTHCARE RETAIL & CPG ENERGY HEALTHCARE MANUFACTURING & LOGISTICS FINANCIAL SERVICES TELECOM & MEDIA Industry Platforms IMMERSIVE EXPERIENCE DESIGN & UX VIDEO SOCIALMEDIA & STORYTELLING DIGITALECOSYSTEM Customer Experience Reply expertise and multiple services Machine Learning Reply | DSCAdria 23 | May 18th
- 6. 6 Who am I? About Reply & Abhisar Bharti Machine Learning Reply | DSCAdria 23 | May 18th a.bharti@reply.de ▪ Machine Reply journey started on Feb, 2022 ▪ Time-series enthusiast ▪ Contributor for research in Structure Literature Review techniques ▪ M.Sc (Data and Knowledge Engineering) ▪ Data storyteller Abhisar Bharti Data Science Consultant
- 7. 7 Agenda Machine Learning Reply | DSCAdria 23 | May 18th About Reply & Abhisar Bharti 1 Monitoring and Machine Learning Operations 2 3 Summary 4 Prometheus and Grafana: Advantages, Limitations, Challenges & Outcomes
- 9. 9 It is time for a questions… Monitoring and Machine learning operations Machine Learning Reply | DSCAdria 23 | May 18th
- 10. 10 Monitoring and Machine Learning Operations Monitoring and Machine learning operations Machine Learning Reply | DSCAdria 23 | May 18th Metrics monitoring ▪ Is the system up? ▪ Is the system down? ▪ Is the system degrading? ▪ Collect relevant data that explains state ▪ Label them ▪ Analyse them
- 11. 11 The Importance of Real-Time Monitoring Monitoring and Machine learning operations Machine Learning Reply | DSCAdria 23 | May 18th Unregulated Lifecycle Wrong Feedback Wrong User Behavior Wrong Model Wrong Pre- dictions Time Business Value Monitored Unmonitored KPI monitoring ▪ Over time unmonitored model deviates from desired results ▪ Wrong prediction can lead to degrading business value
- 12. 12 It is time for a questions (again)… Monitoring and Machine learning operations Machine Learning Reply | DSCAdria 23 | May 18th
- 13. What to monitor? Monitoring and Machine learning operations Latency Operational System (Soft- and Hardware) Data (Context, User Behavior, Predictions) Machine Learning Reply | DSCAdria 23 | May 18th Functional 13 Model (Predictions) IO/Memory/CPU usage System uptime Disk utilization Accuracy Precision, Recall, F1 AUC-ROC RMSE Population Stability Index (PSI) Characteristic Stability Index (CSI) Kolmogorov–Smirnov test Kullback–Leibler divergence
- 14. 14 Agenda Machine Learning Reply | DSCAdria 23 | May 18th About Reply & Abhisar Bharti 1 Monitoring and Machine Learning Operations 2 3 Summary 4 Prometheus and Grafana: Advantages, Limitations, Challenges & Outcomes
- 15. 15 How to monitor? Prometheus and Grafana: Advantages, Limitations, Challenges & Outcomes Maximizing the Value of Your Monitoring Data: Insights and Analysis Machine Learning Reply | DSCAdria 23 | May 18th Alarm Structured / Unstructured ML- System ▪ Open-source monitoring soultion. ▪ Pull based approach. ▪ Easy to configure,deploy and maintain. ▪ Container ready. ▪ Orchestration ready (dynamic config). ▪ Open-source toolfor time series analysis. ▪ Feature for alert notification. ▪ Supportfor wide variety of data source. ▪ Supportfor advanced visualization and metrics support
- 16. 16 Setting up an optimized prometheus Prometheus and Grafana: Advantages, Limitations, Challenges & Outcomes Machine Learning Reply | DSCAdria 23 | May 18th Monitoring A Spring BootApplication, Part 2: Prometheus–Tom Gregory ▪ Influx of huge amount of metrics ▪ Outage in visualization ▪ Scrape time in minutes ▪ Megabytes of data collected per scrape Need for Prometheus optimization
- 17. 17 Sample Dashboard for MLOPS using Grafana Prometheus and Grafana: Advantages, Limitations, Challenges & Outcomes Ways to optimize Prometheus Machine Learning Reply | DSCAdria 23 | May 18th First identify those metrics that are not used in visualization 1 Drop the identified metrics 2 Increase scrape interval 3 Migration to cloud provider like AWS 4
- 18. 18 Challenges Prometheus and Grafana: Advantages, Limitations, Challenges & Outcomes 1Catering the need of diverse stakeholders 2Complexity 3Handling government regulations 4Need for Prometheus optimization Machine Learning Reply | DSCAdria 23 | May 18th
- 19. 19 Agenda Machine Learning Reply | DSCAdria 23 | May 18th About Reply & Abhisar Bharti 1 Monitoring and Machine Learning Operations 2 3 Summary 4 Prometheus and Grafana: Advantages, Limitations, Challenges & Outcomes
- 20. Summary 20 Key Takeaways Overview of current and upcoming collaboration ❑ Overview of Monitoring ❑ Need for Model monitoring ❑ Metrics to monitor Generaloverview 1 ❑ Overview of sample workflow ❑ Introduction to Prometheus and Grafana Monitoringworkflow 2 ❑ Need for Prometheus optimization ❑ Steps to optimize Prometheus ❑ Challenges with Machine Learning operations Optimization and challenges 3 ❑ Design & Implementation of a dashboard ❑ Final performance KPIs Results 4 Machine Learning Reply | DSCAdria 23 | May 18th
- 21. Summary 21 Key Takeaways Overview of current and upcoming collaboration ❑ Overview of Monitoring ❑ Need for Model monitoring ❑ Metrics to monitor Generaloverview 1 ❑ Overview of sample workflow ❑ Introduction to Prometheus and Grafana Monitoringworkflow 2 ❑ Need for Prometheus optimization ❑ Steps to optimize Prometheus ❑ Challenges with Machine Learning operations Optimization and challenges 3 ❑ Design & Implementation of a dashboard ❑ Final performance KPIs Results 4 Machine Learning Reply | DSCAdria 23 | May 18th
- 22. Summary 22 Key Takeaways Overview of current and upcoming collaboration ❑ Overview of Monitoring ❑ Need for Model monitoring ❑ Metrics to monitor Generaloverview 1 ❑ Overview of sample workflow ❑ Introduction to Prometheus and Grafana Monitoringworkflow 2 ❑ Need for Prometheus optimization ❑ Steps to optimize Prometheus ❑ Challenges with Machine Learning operations Optimization and challenges 3 ❑ Design & Implementation of a dashboard ❑ Final performance KPIs Results 4 Machine Learning Reply | DSCAdria 23 | May 18th
- 23. Summary 23 Key Takeaways Overview of current and upcoming collaboration ❑ Overview of Monitoring ❑ Need for Model monitoring ❑ Metrics to monitor Generaloverview 1 ❑ Overview of sample workflow ❑ Introduction to Prometheus and Grafana Monitoringworkflow 2 ❑ Need for Prometheus optimization ❑ Steps to optimize Prometheus ❑ Challenges with Machine Learning operations Optimization and challenges 3 ❑ Design & Implementation of a dashboard ❑ Final performance KPIs Results 4 Machine Learning Reply | DSCAdria 23 | May 18th
- 24. Thank you We are hiring! Send us your cv on: a.bharti@reply.de