Creating and Tracking Tailored Metrics for AI Generated Data in DataRobot’s AI Production: A Guide
Introduction:
CIOs and technology leaders have realized that generative AI use cases require careful monitoring to mitigate potential risks, such as toxicity and incomplete information. DataRobot AI Production offers extensive governance and monitoring functionality for these cases. This article shares an approach to defining and monitoring custom performance metrics for generative AI use cases. It focuses on cost, end-user experience, safety and regulatory compliance, and business value metrics. Example code and practical illustrations are provided for each category. This will help organizations improve the observability of their generative AI solutions and demonstrate clear business value.
Full News:
CIOs and technology leaders are increasingly recognizing the need for careful monitoring when it comes to generative AI (GenAI) use cases. The realization has dawned that these applications come with inherent risks, and strong observability capabilities are required to mitigate them. However, traditional data science accuracy metrics often fall short when it comes to LLMOps (Large Language Model Operations).
LLM outputs require a different set of metrics for monitoring, focusing on factors such as toxicity, readability, personally identifiable information leaks, incomplete information, and most importantly, LLM costs. In customer discussions, quantifying the unknown costs associated with LLMs is often the primary concern.
To address these metrics, a generalizable approach to defining and monitoring custom, use case-specific performance metrics for generative AI use cases has been shared. This approach is specifically designed for deployments monitored with DataRobot AI Production but can be applied to models built with other platforms as well.
DataRobot offers extensive governance and monitoring functionality, with out-of-the-box deployment metrics in categories such as Service Health, Data Drift, Accuracy, and Fairness. However, the focus here is on adding user-defined Custom Metrics to a monitored deployment.
This approach is demonstrated using a logistics-industry example published on DataRobot Community Github. By replicating this example, users can gain hands-on experience and learn how to define and monitor custom metrics for their own use cases.
The monitoring of generative AI use cases can be broken down into four main categories—Total Cost of Ownership, User Experience, Safety and Regulatory Metrics, and Business Value.
In the category of Total Cost of Ownership, the metrics revolve around monitoring the expense of operating the generative AI solution. This involves calculating the direct compute costs for self-hosted LLMs or the cost of each API call for externally-hosted LLMs.
User Experience metrics focus on the quality of the responses from the perspective of the intended end user. This includes monitoring response length, readability, and other factors that contribute to the overall user experience.
Safety and Regulatory Metrics aim to monitor generative AI solutions for content that may be offensive or in violation of the law. For example, metrics can be defined to monitor prompts for abusive language, bias, or PII leaks, as well as the toxicity, bias, and polarity of generative responses.
Finally, Business Value metrics are crucial for demonstrating the clear business value of generative AI solutions. These metrics can help secure long-term funding for use cases and provide a basis for calculating the return on investment.
Once the custom metrics are defined, adding them to a deployment is straightforward using the Custom Metrics tab of DataRobot AI Production. By submitting the necessary information, these custom metrics can provide valuable insights into the performance and impact of generative AI use cases.
In conclusion, by carefully defining and monitoring custom metrics for generative AI use cases, CIOs and technology leaders can gain a deeper understanding of the costs, user experience, safety, and business value associated with these applications. This approach provides a comprehensive framework for successful deployment and management of generative AI solutions.
Conclusion:
CIOs and technology leaders are acknowledging the risks involved in using generative AI (GenAI), and the importance of strong observability capabilities to mitigate these risks. With a need for new metrics, including LLM costs, user-experience, safety, regulatory, and business value metrics, this article offers insights and guidance for monitoring custom performance metrics for GenAI use cases with DataRobot AI Production. For CIOs and technology leaders looking to optimize their generative AI strategies, this article offers a comprehensive guide to defining and monitoring custom metrics for generative AI use cases, helping them navigate the complexities and potential of GenAI with practical, actionable insights. With a focus on key categories like total cost of ownership, user experience, safety and regulatory compliance, and business value, this guide provides a roadmap for leveraging custom metrics to enhance the monitoring and governance of generative AI solutions. By offering a thorough examination of how to define and monitor custom metrics for generative AI use cases, this article provides CIOs and technology leaders with the knowledge and tools to effectively measure and optimize the performance and impact of their GenAI solutions, ultimately maximizing their value and effectiveness in driving business outcomes. Whether they are looking to improve cost management, enhance user experience, ensure safety and compliance, or demonstrate clear business value, this article equips CIOs and technology leaders with the insights and strategies they need to harness the full potential of generative AI and achieve meaningful, measurable results for their organizations.
Frequently Asked Questions:
Frequently Asked Questions about Design and Monitor Custom Metrics for Generative AI Use Cases in DataRobot AI Production
What are custom metrics in generative AI use cases?
Custom metrics in generative AI use cases refer to the specific performance indicators and measurements that are tailored to the unique requirements and goals of the AI model being used. These metrics are designed to evaluate the performance and accuracy of the AI model in generating new data or content.
How do I design custom metrics for generative AI use cases?
To design custom metrics for generative AI use cases, you will need to identify the specific objectives and goals of your AI model, and then develop performance indicators that align with those goals. This may involve creating new metrics or modifying existing metrics to better suit the requirements of the generative AI use case.
What are some common custom metrics used in generative AI use cases?
Common custom metrics used in generative AI use cases may include measures of diversity, novelty, coherence, and realism in the generated output. These metrics are often tailored to the specific domain and application of the generative AI model.
Why is it important to monitor custom metrics in generative AI use cases?
Monitoring custom metrics in generative AI use cases is essential for evaluating the performance and effectiveness of the AI model. By tracking custom metrics, you can ensure that the generative AI is meeting the desired objectives and making adjustments as needed to improve its performance.
How can DataRobot AI Production help with monitoring custom metrics for generative AI use cases?
DataRobot AI Production provides tools and capabilities for designing, implementing, and monitoring custom metrics for generative AI use cases. The platform allows for the creation of custom evaluation functions and the integration of these metrics into the monitoring and management of AI models.
What are some best practices for designing and monitoring custom metrics in generative AI use cases?
Best practices for designing and monitoring custom metrics in generative AI use cases include aligning metrics with specific business objectives, regularly evaluating and updating the metrics, and integrating them into the overall model monitoring and management processes.
How can I ensure that my custom metrics are effective in evaluating the performance of generative AI models?
To ensure that custom metrics are effective in evaluating the performance of generative AI models, it is important to validate the metrics against real-world performance and to continuously assess and refine the metrics based on the insights gained from model monitoring and evaluation.
What role does human judgment play in evaluating the performance of generative AI models?
Human judgment can play a critical role in evaluating the performance of generative AI models, especially in assessing metrics related to coherence, novelty, and realism. Incorporating human judgment into the evaluation process can provide valuable insights and validation for the custom metrics used.
How can I leverage the insights from custom metrics to improve the performance of generative AI models?
Insights from custom metrics can be leveraged to improve the performance of generative AI models by identifying areas for improvement, guiding model development and optimization efforts, and informing the creation of new training data and model enhancements.
What are the potential challenges in designing and monitoring custom metrics for generative AI use cases?
Potential challenges in designing and monitoring custom metrics for generative AI use cases may include defining and quantifying abstract concepts such as diversity and coherence, accounting for subjective elements in human judgment, and ensuring that the metrics are aligned with the specific goals and applications of the AI model.
