How can you monitor the performance of an AI model in production?

In terms of your AI model predictions, the most important thing to monitor is model performance in line with the business metrics you set at the beginning. Using metrics to evaluate model performance only makes sense in context. Don’t ever develop a model for model sake. AI needs to be developed for a business purpose. And when the business AI model has been developed it should be continually monitored against its ability to deliver business benefit. If it does then fine tune it to get more. If it does not - work out why and fix it - or out your time and money into an AI model that does deliver business value instead of ignore sunk cost - go again.

We need to identify what is the measure we will use to monitor performance. In forecast project it can be MAPE in classification it can be F1 score, precision, recall etc. Then we need to define the acceptable range. For example if the accuracy is within 90-95% it is acceptable. If accuracy falls below this range, then it's a concern. For me post deployment monitoring of Generative AI is expensive, since I believe in human validation of accuracy. Hence, on periodic basis, my stakeholders/clients have to manually validate the output.

Before monitoring your AI model, you need to define your objectives and criteria for success. This means identifying the main goals and outcomes that you want to achieve with your model, and deciding how you will measure them. You should also set some acceptable ranges and thresholds for your metrics, so that you can quickly identify when something goes wrong. Here are some tips for defining your objectives and criteria for success: Start with your business goals. What are you hoping to achieve by using an AI model? For example, do you want to increase sales, improve customer satisfaction, or reduce costs? Once you know your business goals, you can start to define specific objectives for your AI model.

It's imperative to have clear objectives before diving into monitoring an AI model in production. Defining what success looks like for your model steers the monitoring process in a direction that's aligned with your goals. For instance, if the model is aimed at customer sentiment analysis, metrics like accuracy, precision, recall, and F1-score become critical. These metrics will guide you in tuning your model for better performance. Furthermore, setting acceptable ranges for these metrics helps in creating a benchmark for model performance. In my view, a well-defined objective is like a lighthouse, it guides you amidst the vast ocean of data and algorithms, ensuring that your AI model delivers value and achieves its intended purpose.

?? Keep a Vigilant Eye on AI Performance! I'd advise organizations to proactively set clear objectives and success metrics for their AI models. Regularly collecting and analyzing data from varied sources, such as logs and feedback, can provide actionable insights into the model's operation. Periodically review performance metrics to spot changes or anomalies and employ tools like Fiddler AI, Arize AI, and TensorFlow for efficient monitoring. Adequate training of staff and users is essential for ensuring model security and prompt issue resolution. By adhering to these practices, companies can maximize their AI model's efficiency and security.

Data Collection: a. Data Sources: Identify the sources from which you will collect data. This could include databases, online repositories, APIs, or custom data collection methods such as surveys or user interactions. b. Data Quality: Ensure the data collected is accurate, consistent, and representative of the problem domain. Address any issues related to missing, incomplete, or erroneous data. c. Data Privacy and Ethics: Anonymize or aggregate sensitive information to protect user privacy. d. Data Labelling (Supervised Learning): If your AI model requires labelled data for training (as in supervised learning), invest time in accurate labelling. Consider crowdsourcing or working with domain experts for high-quality annotations.

Analyze input data by checking distribution of metric values, relationships between metrics, if data for specific region/product category is not made available etc. Also have to analyze output performance to check for performance degradation. Have to monitor logs to check if there are errors or warnings. Check for time spent in running the codes has changed. It's important to host this analysis in a visualization solution and make it available to business SME and technical lead. Performance can also drop if the data quality degrades over time. If the data format changes, then performance can degrade, or even the model might stop working. There can issues with infrastructure like being overloaded etc.

There are a number of reasons why it is important to monitor AI models in production: Model drift: Over time, the real-world data that the model is exposed to may change in ways that the model was not trained on. This can lead to a decrease in the model's performance, known as model drift. Data quality: The quality of the data that the model is fed can also have a significant impact on its performance. If the data is incomplete, inaccurate, or biased, the model will not be able to perform as well. System performance: The performance of the system that the model is deployed on can also affect its performance. If the system is overloaded or experiences latency, the model's predictions may be delayed or inaccurate.

Collecting and analysing data are the cornerstone steps for monitoring an AI model effectively. By scrutinizing both input and output data alongside system metrics like CPU usage and latency, you can spot issues or areas for improvement. For instance, an unexpected spike in latency might indicate a bottleneck that needs addressing. Besides collecting data, it's pivotal to have a structured analysis strategy. Incorporating feedback loops for continuous model improvement and employing visualization tools for easier data interpretation are prudent steps. This holistic approach ensures that your AI model remains robust, efficient, and aligned with your objectives post-deployment.

Establish a baseline performance level during the initial deployment. This serves as a reference point for future comparisons.

Collected data need to be used to analyze model performance now as compared to earlier and can be compared with benchmark. The input data's metric distribution need to be compared now versus earlier, use statistics to check if the relationship between metrics have changed now as compared to earlier. If there is a degradation in model performance, check if it still falling within range. Also we can check if there is a continuous degradation in performance, if yes, then it's a concern and root cause need to be first identified.

Once you have collected data on your AI model's performance, you need to compare it to your objectives and criteria for success. Use statistical and analytical techniques to identify trends, patterns, anomalies, or changes in your metrics over time or across different segments or groups. Assess the impact and significance of any deviations or discrepancies in your metrics, and determine whether they are due to data quality, model drift, bias, errors, or other factors.

- For instance, consider a recommendation system for an e-commerce platform. - One key metric to monitor is the click-through rate (CTR). - By tracking CTR over time and across different user segments, we can detect changes or anomalies. - If CTR decreases in a specific segment, it might indicate a problem like model drift, which requires immediate attention. - Monitoring metrics like CTR using statistical and analytical techniques helps ensure your AI model remains on the right track. - Monitoring the performance of an AI model in production is crucial to ensure its continued effectiveness. #AI #MachineLearning #ProductionMonitoring #Metrics #ModelDrift #DataQuality #Analytics #CTR #ClickThroughRate

Once evaluation is done, and if performance has dropped below acceptable range, then retraining is needed. Need to identify the root cause for degradation, and then fix it. The fix can be change in hyperparameters, explore new models, dropping variables, data transformations etc. Once the fix is done, need to check if the performance has improved. Robust checking / validation needs to be done using k-fold samples or other methods to ensure performance has indeed improved.

Based on your analysis and evaluation, take action to improve your AI model as needed. Decide whether to retrain, update, or optimize your model, or to change your data sources, features, or parameters. Implement and test your changes carefully, and monitor their effects on your metrics. Document and communicate your actions and results, and gather feedback and validation from stakeholders. I made the following changes: I removed the phrase "you need to" to make the paragraph more concise and actionable. I replaced the phrase "if needed" with the more concise phrase "as needed."

Perform error analysis to understand the types of mistakes the model makes. This can help in identifying patterns and areas for improvement.

- Deploying your model is just the beginning of the journey. - Monitoring is only the first part; action and improvement are equally vital. - After analyzing metrics and detecting anomalies, you must take decisive steps. - For instance, if you identify a drop in image recognition accuracy in a computer vision model used in autonomous vehicles, it might indicate a drift in the model due to changing road conditions. - In response, you'd need to retrain the model using updated data to adapt to new scenarios. - Taking these actions and maintaining open communication with stakeholders is key to achieving long-term AI success. #AI #MachineLearning #DataScience #AIinProduction #PerformanceMonitoring

We need to automate monitoring of input data and monitoring of output performance. One also should automate the process of capturing infrastructure parameters like CPU usage, cost incurred etc. and bring it in a dashboard. Monitoring manually can be very time consuming. Also some AI solution outputs can't be monitored manually. For example if we are predicting monthly sales of a product (CKU) and there are thousands of SKUs, then we can monitor prediction performance at SKU level (yes we can aggregate SKUs and then measure performance manually)

Monitoring your AI model in production can be a daunting task. To save time and resources, automate as much of the process as possible, including data collection, analysis, evaluation, and action. Use tools and frameworks that can alert you when metrics fall outside predefined ranges or thresholds. Configure and customize your automation and alert settings to meet your specific needs.

- One crucial approach is to automate and set up alerts. - With automation, you can streamline data collection, analysis, and evaluation, saving valuable time and resources. - Setting up alerts using tools and frameworks allows you to promptly respond when metrics deviate from predefined ranges or thresholds. - For example, you can configure alerts to detect anomalies in a recommendation system's click-through rate and trigger corrective actions, such as model retraining, when necessary. - Stay proactive in AI model performance monitoring with automation and alerts. #AI #MachineLearning #DataScience #AIModel #PerformanceMonitoring

Monitoring AI models in production is an ongoing process. You need to regularly review and improve your monitoring approach to ensure it is effective and efficient. This includes checking that your goals and criteria are still relevant and achievable, and that your metrics and tools are appropriate and reliable. As your model, data, and environment change, you need to update and refine your monitoring process accordingly. Learn from your experience and feedback, and continuously improve your AI model and its performance.

- First, compare and evaluate metrics against objectives and criteria, employing statistical and analytical techniques to detect trends, patterns, anomalies, and changes. - For instance, tracking an image recognition model's accuracy over time can reveal fluctuations, helping you identify areas for improvement. - Furthermore, monitoring is not a one-and-done task; it's an ongoing process. - Regularly review and iterate on your monitoring approach to adapt to evolving models, data, and environmental factors. - Stay agile and learn from your experiences to continually enhance your AI model's performance. #AI #MachineLearning #DataScience #AIinProduction #ModelMonitoring #Metrics #ContinuousImprovement

Define your objectives and criteria for success. Collect and analyze data on your model's performance. Track key metrics and identify trends and patterns. Take action to address any performance issues. Automate and alert on key metrics. Review and iterate your monitoring process regularly.

In any digital project, you need to follow the steps mentioned in this article (objectives, analyze data, metrics, take action, automate, alert, review and iterate), Here to summarize I would say that to monitor the performance of an AI model in production, follow these steps. Moreover, implement continuous monitoring tools that track model predictions against real-world outcomes. Set up automated alerts for significant deviations from expected performance or accuracy metrics. Regularly review dashboards that visualize key performance indicators, such as precision, recall, and latency. Concurrently, gather feedback from end-users and periodically retrain the model with fresh data to ensure its relevancy and efficacy in changing environments

Conduct A/B testing to compare the performance of different model versions and configurations. This helps in identifying improvements and selecting the best-performing model.

Monitoring quality performances should be considered when designing the overall architecture of the system. For example, using smaller, expert models with narrower use cases will help monitor: Coupling classifiers to generative models is already an industry standard. For example, to remove the risk of generating adult content (text or image), fine tuning a large model is not enough and you would have to add classifiers to completely remove risks. Then, you can feed the outputs of these smaller models to both the downstream user or to an alert system.