ChatGPT: Performance Monitoring
and Optimization

As artificial intelligence (AI) continues to revolutionize industries, tools like ChatGPT have become indispensable for automating tasks, enhancing customer interactions, and improving operational efficiency. However, the effectiveness of these AI models relies heavily on continuous performance monitoring and optimization to ensure they meet user expectations, respond efficiently, and deliver accurate information across various applications. This training course is designed to equip professionals with the knowledge and skills to effectively monitor, analyze, and enhance the performance of ChatGPT in different contexts.

In this course, participants will learn how to set up monitoring frameworks that track key performance metrics, such as response time, accuracy, user satisfaction, and cost-efficiency. They will explore advanced optimization techniques to fine-tune the model's responses, reduce latency, and ensure scalability in high-traffic environments. Additionally, this course covers troubleshooting common issues that arise during model deployment, helping participants address response errors, data biases, and other performance bottlenecks.

With the growing reliance on conversational AI in customer service, content generation, and decision support, ensuring the seamless performance of ChatGPT has never been more critical. Participants will gain hands-on experience using the latest tools and methodologies to continuously improve the performance of AI-driven systems, positioning them to create more intelligent, responsive, and efficient AI applications.

This training is tailored for AI and machine learning engineers, data scientists, developers, and AI product managers who are involved in deploying and maintaining AI models in real-world applications. Whether you're optimizing ChatGPT for customer service automation or developing cutting-edge conversational tools, this course will provide the expertise needed to maintain high standards of performance and reliability.

This online training course will feature:

• Establish real-time monitoring systems to track ChatGPT’s performance in live environments
• Understand the various metrics used to measure model performance and identify areas for improvement
• Apply advanced optimization strategies to enhance response quality, reduce operational costs, and ensure scalability
• Troubleshoot performance issues, such as latency, incorrect responses, and token overuse
• Scale ChatGPT deployments for larger user bases without compromising performance

By the end of this online training course, participants will be able to:

• Understand the performance metrics and KPIs relevant to ChatGPT
• Identify bottlenecks and optimize model performance for specific tasks
• Implement monitoring tools to track model health and performance in real-time
• Analyze user interactions to fine-tune response quality
• Apply troubleshooting techniques to improve model efficiency and reduce errors
• Enhance scalability and performance for larger workloads

This Anderson online training course is suitable to a wide range of professionals but will greatly benefit:

• AI and machine learning engineers
• Data scientists
• AI product managers
• Developers working with conversational AI
• Customer service professionals using ChatGPT for automation
• Technical team leads overseeing AI-driven projects

This Anderson training course will utilise a variety of proven adult learning techniques to ensure maximum understanding, comprehension and retention of the information presented. This includes an interactive mixture of lecture-led learning & group discussions.

• Overview of ChatGPT architecture and components
• Understanding key performance metrics (accuracy, latency, token usage)
• KPIs for specific use cases (customer service, content generation, etc.)
• Tools and platforms for tracking model performance
• Case studies on real-world performance monitoring

• Setting up real-time monitoring for ChatGPT
• Using APIs and dashboards for performance tracking
• Monitoring token consumption and cost-effectiveness
• Automated alerts and notifications for performance drops
• Hands-on exercise: Setting up monitoring for a ChatGPT instance

• Fine-tuning model responses for accuracy and efficiency
• Reducing latency and improving response times
• Memory management and token optimization
• Implementing caching and load balancing
• Hands-on exercise: Optimizing ChatGPT performance for a specific task

• Common performance issues and how to resolve them
• Debugging unexpected responses and errors
• Handling data biases and improving response relevance
• Analyzing failed interactions and model errors
• Hands-on exercise: Troubleshooting a performance issue

• Scaling ChatGPT for large workloads and high user traffic
• Managing infrastructure for optimal performance
• Integrating ChatGPT with other AI models and tools
• Future trends in ChatGPT performance optimization
• Final assessment and feedback session

An Anderson e-Certificate will be provided to delegates who attend and complete the online training course