Build a robust foundation for model development, Well Architected framework , AI Perspective : Part 2

Implement observability

The behavior of AI and ML systems can change over time due to changes in the data or environment and updates to the models. This dynamic nature makes observability crucial to detect performance issues, biases, or unexpected behavior. This is especially true for generative AI models because the outputs can be highly variable and subjective. Observability lets you proactively address unexpected behavior and ensure that your AI and ML systems remain reliable, accurate, and fair.

You can use Vertex AI Model Monitoring to proactively track model performance, identify training-serving skew and prediction drift, and receive alerts to trigger necessary model retraining or other interventions. To effectively monitor for training-serving skew, construct a golden dataset that represents the ideal data distribution, and use TFDV to analyze your training data and establish a baseline schema.

Configure Model Monitoring to compare the distribution of input data against the golden dataset for automatic skew detection. For traditional ML models, focus on metrics like accuracy, precision, recall, F1-score, AUC-ROC, and log loss. Define custom thresholds for alerts in Model Monitoring. 

You can also enable automatic evaluation metrics for response quality, safety, instruction adherence, grounding, writing style, and verbosity. To assess the generated outputs for quality, relevance, safety, and adherence to guidelines, you can incorporate human-in-the-loop evaluation.

Use Vertex AI rapid evaluation to let Google Cloud automatically run evaluations based on the dataset and prompts that you provide.

Use adversarial testing to identify vulnerabilities and potential failure modes. To identify and mitigate potential biases, use techniques like subgroup analysis and counterfactual generation. Use the insights gathered from the evaluations that were completed during the development phase to define your model monitoring strategy in production. Prepare your solution for continuous monitoring as described in the Monitor performance continuously section of this document

Monitor for availability

To gain visibility into the health and performance of your deployed endpoints and infrastructure, use Cloud Monitoring. For your Vertex AI endpoints, track key metrics like request rate, error rate, latency, and resource utilization, and set up alerts for anomalies. For more information, see Cloud Monitoring metrics for Vertex AI.

Monitor the health of the underlying infrastructure, which can include Compute Engine instances, Google Kubernetes Engine (GKE) clusters, and TPUs and GPUs. Get automated optimization recommendations from Active Assist. If you use autoscaling, monitor the scaling behavior to ensure that autoscaling responds appropriately to changes in traffic patterns.

For timely identification and rectification of anomalies and issues, set up custom alerting based on thresholds that are specific to your business objectives. Examples of Google Cloud products that you can use to implement a custom alerting system include the following:

Cloud Logging: Collect, store, and analyze logs from all components of your AI and ML system.

Cloud Monitoring: Create custom dashboards to visualize key metrics and trends, and define custom metrics based on your needs. Configure alerts to get notifications about critical issues, and integrate the alerts with your incident management tools like PagerDuty or Slack.

Error Reporting: Automatically capture and analyze errors and exceptions.

Cloud Trace: Analyze the performance of distributed systems and identify bottlenecks. Tracing is particularly useful for understanding latency between different components of your AI and ML pipeline.

Cloud Profiler: Continuously analyze the performance of your code in production and identify performance bottlenecks in CPU or memory usage.

Prepare for peak events

Ensure that your system can handle sudden spikes in traffic or workload during peak events. Document your peak event strategy and conduct regular drills to test your system's ability to handle increased load.

To aggressively scale up resources when the demand spikes, configure autoscaling policies in Compute Engine and GKE. For predictable peak patterns, consider using predictive autoscaling. To trigger autoscaling based on application-specific signals, use custom metrics in Cloud Monitoring.