Shadow Deployment for AI Model Testing

Testing AI models in production is challenging because real-world data is unpredictable, and you can't risk disrupting live systems. Shadow deployment solves this by running your new model alongside the live system, processing the same requests but never exposing its responses to users. This method lets you safely test performance, identify issues, and evaluate resource usage under production conditions without impacting the user experience.

Key Takeaways:

• How It Works: Duplicate live traffic to a shadow model for testing without affecting users.
• Why Use It: Safely identify model errors, edge cases, and bottlenecks in production-like scenarios.
• Infrastructure Needs: Scalable systems, traffic mirroring tools (e.g., NGINX Plus, AWS ALB), and robust logging solutions.
• Cost Implications: Increased compute, storage, and data transfer expenses, but selective testing can help manage costs.
• Testing Metrics: Monitor latency, accuracy, resource consumption, and error rates to evaluate readiness.

Shadow deployment is a low-risk method to validate AI models in near-live conditions, ensuring stability and performance before a full rollout. With proper tools and monitoring, it helps teams refine models while maintaining production integrity.

AWS re:Invent 2022 - Minimizing the production impact of ML model updates w/shadow testing (AIM343)

Prerequisites for Implementing Shadow Deployment

Before diving into shadow deployment for testing AI models, you need to ensure your systems are ready to handle the extra workload. This requires thorough planning and scaling your infrastructure to manage the additional traffic created by duplicating live requests.

Infrastructure Requirements

Shadow deployment involves running your live production model and the shadow model side by side, which can almost double the processing load. To manage this, your infrastructure has to be capable of handling the increased demand.

A load balancer or API gateway with traffic mirroring capabilities is essential. These tools duplicate incoming traffic, sending copies to both the production model and the shadow environment. Popular options include NGINX Plus, HAProxy, AWS Application Load Balancer, and Google Cloud Load Balancing.

Your compute resources should also align with your production setup. For instance, if your live model requires specific CPU, memory, or GPU configurations, ensure that the shadow environment is equipped with comparable resources.

Additionally, you’ll need to plan for increased storage. Logging requests and responses for both environments will consume more space, so allocate extra storage and ensure your network can handle the higher data transfer demands.

Once your infrastructure is ready, you’ll need the right tools to manage and monitor your shadow deployment effectively.

Tools and Technologies

A variety of tools can help you manage traffic, monitor performance, and streamline the deployment process. Service mesh technologies like Istio, Linkerd, or Consul Connect are particularly useful for splitting network traffic without altering your application code.

For monitoring, observability tools are a must. A combination like Prometheus and Grafana provides real-time metrics and visualisations, enabling you to track the performance and resource usage of both your production and shadow models.

Increased logging requires robust solutions like the ELK Stack or Fluentd with OpenSearch to handle the higher log volumes.

If you’re using containerised environments, platforms like Kubernetes allow you to quickly spin up shadow environments and dynamically manage resources. Integrating shadow deployment into your CI/CD pipeline with tools such as Jenkins, GitLab CI, or GitHub Actions can further streamline the process, ensuring automated and consistent deployments.

With these tools in place, it’s also crucial to evaluate and manage the associated costs.

Cloud Cost Considerations

Shadow deployments can significantly impact your cloud expenses, as they essentially duplicate much of your infrastructure. Beyond the increased compute and GPU requirements, you’ll also face higher costs for data transfer and storage due to the additional logging and duplicated traffic.

For example, GPU instances and the extra data handling can quickly add up, making cost management a key factor. Companies like Hokstad Consulting offer cloud cost engineering services to help optimise resource allocation and monitor expenses. Their expertise can help you maintain the benefits of shadow testing while keeping costs manageable.

To save on costs, consider running shadow deployments selectively - such as during peak traffic periods or before major releases. This approach allows you to gather valuable insights without incurring unnecessary expenses.

Step-by-Step Guide to Implementing Shadow Deployment

Once your infrastructure is set up and your tools are ready, you can begin implementing shadow deployment. This process involves carefully managing traffic routing, deploying models, and setting up safeguards to ensure your production systems remain unaffected.

Configuring Traffic Mirroring and Request Duplication

Start by enabling traffic mirroring to duplicate incoming requests. This allows you to create exact copies of production requests and send them to your shadow environment while maintaining uninterrupted service for live users.

For traffic mirroring, configure your load balancer accordingly. If you're using NGINX Plus, the mirror directive can forward duplicated requests. On AWS ALB, create a separate target group and enable mirroring. Begin with a small portion of traffic, such as 10%, and gradually increase it as needed.

Focus on mirroring only the traffic that’s relevant to your testing. This might mean targeting specific request types, user segments, or patterns based on headers. As part of this process, ensure sensitive data is masked or sanitised to avoid unnecessary exposure.

Timeout settings are also crucial. Shadow requests should have reasonable timeouts to prevent them from straining your infrastructure. This ensures meaningful testing without overloading your resources.

Once traffic mirroring is configured, deploy your shadow model to handle these mirrored requests.

Deploying the Shadow Model

Deploy the shadow model in an isolated namespace using Kubernetes. This separation ensures that resource allocation, networking, and configurations for the shadow model don't interfere with your production environment.

Aim to match the shadow model’s resource allocations with those of production, but you can start smaller to save costs. For instance, begin with a single instance and scale up as needed.

Ensure the shadow model mirrors your production setup in terms of configuration and versioning. Use clear naming conventions, such as v2.1.0-shadow or candidate-release-shadow, to differentiate test versions from production.

Before sending traffic to the shadow model, perform health checks and readiness probes. These checks verify that the model can process requests and return valid responses, ensuring it’s ready for testing.

Once deployed, implement safeguards to handle potential errors and failures effectively.

Setting Up Error Handling and Fail-Safes

Error handling is critical to ensure that issues in the shadow deployment don't affect live user traffic. Here’s how to set this up:

• Asynchronous Processing: Shadow requests should be processed in the background, so live users aren’t impacted by shadow model latency or failures. The production system should serve users immediately, regardless of shadow performance.

• Circuit Breakers: Configure circuit breakers to stop sending traffic to the shadow model if error rates exceed a certain threshold. For example, if error rates surpass 50% over a 5-minute period, the circuit breaker can pause shadow traffic until the issue is resolved.

• Resource Isolation: Use Kubernetes resource constraints or cloud provider limits to set maximum CPU, memory, and network usage for the shadow environment. This prevents a malfunctioning shadow process from affecting production resources.

• Monitoring and Alerting: Set up separate monitoring and alerting systems for the shadow environment. Track error rates, latency spikes, or unusual resource consumption, and ensure these alerts are distinct from production monitoring to avoid confusion.

• Automatic Shutdown: Add an automatic shutdown mechanism as a final safety measure. If error rates remain high, resource usage exceeds limits, or manual intervention is needed, the shadow deployment should automatically shut down. This action should be logged and reported to the development team for analysis.

• Error Logging and Isolation: Keep shadow deployment logs separate from production logs. This prevents shadow logs from overwhelming production systems and makes it easier to analyse shadow model performance independently.

Finally, include rollback procedures in your fail-safe setup. This could involve updating load balancer configurations, scaling shadow deployments to zero replicas, or temporarily halting traffic mirroring. These steps ensure you can quickly disable shadow deployment without disrupting production.

Monitoring and Analysing Model Performance

Ensuring safe production testing means keeping a close eye on your model's performance. Monitoring plays a crucial role in shadow deployment testing, acting as the foundation for evaluating whether your model is ready for production. Without proper tracking and analysis, you risk missing essential insights about how your AI model performs and whether it meets the necessary standards. The aim is to gather detailed data that supports informed decisions while maintaining the integrity of your production environment.

Key Metrics to Monitor

When monitoring your shadow deployment, focus on these critical metrics:

• Response time and latency: Compare metrics from the shadow and production models. Look at both average response times and high-percentile values to identify stress-related performance issues.

• Prediction accuracy: Measure accuracy by comparing shadow model outputs with ground truth data or production model predictions. For classification models, track precision, recall, and F1 scores across all classes. For regression models, monitor metrics like mean absolute error (MAE) and root mean square error (RMSE). Automate these calculations hourly and store them in your monitoring system for easy access.

• Resource consumption: Keep an eye on CPU, memory, and network usage. Unusual patterns, such as a 40% increase in memory usage, might indicate inefficiencies or hidden problems.

• Throughput metrics: Assess how many requests your shadow model can handle compared to the production model. For instance, if the production model processes 1,000 requests per second but the shadow model caps at 600, adjustments are needed before moving forward.

• Error rates and types: Group errors into categories like timeouts, prediction failures, or system errors. A shadow model showing a 5% timeout error rate might need infrastructure improvements or code optimisation, while prediction failures could point to data quality or training issues.

Logging and Offline Analysis

Structured logging is essential for detailed offline analysis. Your logs should include timestamps, input features, model predictions, confidence scores, and processing times. Using a format like JSON makes parsing and analysis more straightforward.

• Separate logging infrastructure: To avoid overwhelming production systems, set up dedicated storage for shadow model logs, such as a separate S3 bucket or database table. This separation simplifies performance analysis without interference from production logs.

• Batch processing for comparisons: Use batch jobs to periodically compare shadow and production outputs. These jobs can calculate statistical differences, detect prediction drift, and flag unusual patterns for further investigation.

• Data sampling strategies: For high-traffic systems, consider sampling every 10th request or using stratified sampling based on request characteristics. This reduces storage costs while maintaining a representative dataset for analysis.

• Retention policies: Balance storage costs with analytical needs. Keep detailed logs for 30 days for immediate analysis, then archive summarised metrics for long-term trend tracking. This approach ensures you have enough data for decision-making without unnecessary expenses.

Real-Time Alerts and Thresholds

Real-time alerts are critical for catching issues as they happen. Configure alerts for specific scenarios, such as response time spikes (e.g., 50% above production), error rates exceeding 2%, or excessive resource usage. Use tiered notifications - minor issues can go to Slack, moderate ones to email, and critical alerts to phone notifications.

• Comparison-based alerting: Instead of relying solely on fixed thresholds, set alerts to trigger if the shadow model's accuracy drops more than 5% below production levels or if prediction confidence scores show significant shifts. This approach helps uncover subtle problems.

• Alert correlation: Reduce noise by grouping related alerts. For example, if high response times and increased memory usage occur simultaneously, your system should highlight a potential memory leak in the shadow model.

• Automated remediation: Automate responses to common issues. For instance, scale down shadow deployments if resource usage exceeds limits, or temporarily pause traffic mirroring if error rates spike. These measures protect your infrastructure while logging details for later review.

Promoting the Shadow Model to Production

Moving a shadow model into production requires a careful, phased approach to ensure a smooth transition. This process builds on the insights gained during testing and focuses on meeting strict criteria before deployment. At Hokstad Consulting, we encourage organisations to meticulously assess promotion requirements to ensure a successful rollout.

Criteria for Promotion

Establish clear and measurable benchmarks to ensure the shadow model is ready for production. These benchmarks should reflect business goals and confirm that the new model consistently outperforms the current production system.

The shadow model must demonstrate reliable performance across key metrics, including matching or exceeding the production system's accuracy, maintaining comparable response times, and achieving low error rates even during peak usage. Stability is critical - testing should cover a range of traffic patterns, from quiet periods to heavy loads, to ensure the model can handle unpredictable scenarios effectively.

Efficiency is another key factor. Compare the shadow model's resource usage - such as CPU and memory - against the production system. If the shadow model demands significantly more resources without delivering proportional benefits, further optimisation may be necessary. Keep a close eye on cloud costs, especially when scaling up to handle full production traffic.

The business impact must also be validated. For instance, in a recommendation system, monitor user engagement, while in fraud detection, assess false positives alongside detection accuracy. Document these criteria in a checklist to maintain accountability and streamline the evaluation process.

Gradual Rollout Strategies

A staged rollout minimises risk and provides valuable insights. Start with a canary release, where only a small portion of production traffic is routed to the new model, while the majority continues using the current system. This cautious approach helps validate performance under real-world conditions.

Increase traffic to the new model incrementally. Allow enough time at each stage to observe how the model behaves under varying usage patterns. This step-by-step method ensures the system remains stable as it takes on greater loads.

For global applications, consider geographic rollouts. Start by deploying the model in a single region - preferably one with less business impact. Once the model proves itself in this region, expand gradually to other markets.

Another option is user segment rollouts, beginning with internal users or beta testers. These groups can provide detailed feedback before the model is introduced to less active users and, eventually, the most engaged ones. This approach protects critical user groups while gathering diverse performance data.

Use feature flags to maintain flexibility. These controls allow dynamic traffic routing between the old and new models, making it easier to adjust quickly if issues arise. Throughout the process, monitor key metrics like conversion rates, session durations, and error reports to gain insights that may not surface through technical monitoring alone.

Rollback Procedures

A solid rollback plan is essential for addressing unexpected issues during deployment. If metrics fall outside acceptable thresholds, an automated rollback can be triggered. Alternatively, follow a detailed manual process if needed.

Create comprehensive runbooks that outline every step of the rollback process. These should include instructions for database updates, configuration changes, and traffic redirection. Regularly test these procedures to ensure they function as intended.

In some cases, a phased rollback may be more effective than an immediate switch back to the old model. Gradually reducing the new model's traffic share can help identify whether the problem is related to traffic volume or deeper model flaws.

During rollbacks, continue logging data from both the old and new models. This information is invaluable for diagnosing issues and refining future deployment strategies.

Integrate rollback procedures into ongoing performance reviews to improve deployment processes continuously. Establish clear escalation paths and notification protocols to keep business teams informed about user experience impacts. After a rollback, conduct a thorough analysis to document what went wrong, how it was addressed, and what can be improved for the future.

Preparing robust rollback procedures in advance not only mitigates risks but also builds confidence in the organisation's ability to innovate and enhance its AI systems effectively.

Key Takeaways

Shadow deployment removes the uncertainty often associated with synthetic tests by allowing new models to process real operational traffic. This ensures testing conditions closely resemble live scenarios, providing a more accurate assessment of model performance [1].

While the live system continues to serve users without interruption, the shadow model handles the same requests in parallel. This setup helps identify performance issues or bottlenecks before they affect actual users [2].

One of the standout advantages of this method is its ability to provide measurable insights into resource usage. Instead of relying on estimates for CPU, memory, or storage needs, shadow deployment delivers precise data on utilisation patterns. This helps optimise infrastructure costs and avoids over-provisioning [1].

Another key benefit is the ability to roll back instantly. Teams can experiment with confidence, knowing they can revert to the existing model immediately if any problems arise, maintaining system stability [2].

Beyond identifying issues, shadow deployment is also a powerful tool for validating improvements. Whether you're introducing a new algorithm or tweaking an existing one, this method ensures the updated model meets or exceeds current performance standards. It also validates the entire inference pipeline and serving infrastructure under real-world conditions [3][4].

For organisations prioritising AI reliability, shadow deployment shifts the focus from speculative testing to data-backed assurance. By offering zero risk to users, real-world performance validation, and detailed insights, it becomes an essential approach for maintaining robust and competitive AI systems. Not only does it protect production integrity, but it also enables cost-effective optimisation of AI performance. If you're looking to integrate shadow deployments into your AI strategy or reduce cloud expenses, Hokstad Consulting provides expert guidance tailored to your needs.

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