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03/11/2024Strategic IT Governance and Operational Excellence for Modern Enterprises
05/11/2024Performance Monitoring and Optimization
£4,500.00
Overview:
In the rapidly evolving field of AI, ensuring optimal performance and scalability of AI models like ChatGPT is essential. This course provides an in-depth exploration of tools, metrics, and strategies for monitoring, troubleshooting, and optimizing AI systems in real-time. Participants will gain hands-on experience with industry-standard monitoring frameworks like Prometheus, Grafana, Datadog, and New Relic, alongside methods for prompt engineering and scaling AI deployments to handle high demand. The course is designed to equip attendees with the skills needed to maintain reliable, efficient, and cost-effective AI performance in production environments.
Program Objectives:
At the end of this program, participants will be able to:
- Understand and implement essential performance metrics for monitoring AI systems.
- Set up real-time monitoring and alert systems using Prometheus, Grafana, and Datadog.
- Employ automated monitoring solutions and low-code tools for efficient tracking.
- Troubleshoot and resolve common performance issues in ChatGPT and similar deployments.
- Optimize prompt engineering and resource usage for improved performance and cost efficiency.
- Scale AI deployments to meet increased demand while maintaining system reliability.
- Develop a continuous monitoring framework that incorporates AI-driven anomaly detection for proactive issue management.
Target Audience:
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- AI and Machine Learning Engineers
- IT Professionals and System Administrators
- DevOps Engineers and Cloud Architects
- Data Scientists and AI Specialists
- Business Analysts and Decision-Makers
- Entrepreneurs and Business Leader
Program Outline:
Day 1: Foundations of Performance Monitoring in AI Systems
- Introduction to Performance Monitoring in AI – Why It Matters and Key Concepts.
- Core Performance Metrics for AI Monitoring – Latency, Throughput, Error Rates, and Resource Utilization.
- Overview of Monitoring Tools: Prometheus, Grafana, Datadog, and New Relic.
- Case Studies of Effective Performance Monitoring in AI Systems.
- Hands-On Exercise: Setting Up Basic Monitoring for a ChatGPT Deployment Using Prometheus and Grafana.
- Reflection & Review: Discussing Key Metrics and Monitoring Tools in Real-World Scenarios.
Day 2: Real-Time Performance Tracking and Custom Dashboards
- Implementing Real-Time Tracking for ChatGPT and Similar AI Deployments.
- Techniques for Log Analysis to Gain Deeper Insights into AI Performance.
- Building Custom Dashboards to Visualize Performance Metrics in Real Time.
- Using Prometheus and Grafana for Setting Up Alerts and Notifications.
- Hands-On Exercise: Creating a Custom Real-Time Monitoring Dashboard for AI Systems.
- Reflection & Review: Best Practices for Real-Time Monitoring and Visual Analysis.
Day 3: Troubleshooting and Error Handling in AI Systems
- Identifying and Diagnosing Common Performance Issues in AI Systems.
- Implementing Error-Handling Strategies – Retries, Fallback Mechanisms, and User Notifications.
- Conducting Root Cause Analysis Using Logs and Performance Data.
- Case Studies on Troubleshooting in High-Impact AI Environments.
- Hands-On Exercise: Simulating Errors and Practicing Troubleshooting Techniques.
- Reflection & Review: Lessons from Real-World Scenarios in AI Troubleshooting.
Day 4: Optimization Techniques for AI Performance
- Techniques for Optimizing Prompt Engineering and Response Quality.
- Fine-Tuning and Model Adjustment Strategies for Enhanced Performance.
- Scaling ChatGPT Deployments to Handle Increased Demand.
- Cost-Efficient Scaling and Resource Optimization with Cloud Solutions (e.g., AWS, GCP).
- Hands-On Exercise: Applying Prompt Optimization and Scaling Techniques in a Live Deployment.
- Reflection & Review: Long-Term Strategies for AI Performance Enhancement and Cost Management.
Day 5: Continuous Monitoring, Automation, and Future Trends
- Building a Continuous Monitoring Framework for Long-Term AI Performance Management.
- Automating Monitoring Processes Using Tools and Custom Scripts.
- Emerging Trends in AI Monitoring – Anomaly Detection, Predictive Analytics, and AI-Driven Insights.
- Industry Case Studies on Sustainable Performance Optimization Practices.
- Capstone Project: Designing and Deploying a Comprehensive Monitoring and Optimization Framework for AI.
- Reflection & Review: Project Presentations, Peer Feedback, and Discussion on Future Trends in AI Monitoring.