The digital era is changing because of the extensive use of Artificial Intelligence (AI) and Machine Learning (ML) techniques. AI and ML are noteworthy in empowering essential applications, services, and national critical infrastructures. From personalized healthcare systems to fraud detection systems, from recommendation engines to autonomous vehicles - companies use AI models to make real-time decision-making. Witnessing the tremendous use of AI across every vertical and solution, it has become essential for enterprises to ensure the resilience and reliability of AI systems under adverse conditions. Any form of system failure causes costly outages for enterprises.

That is when enterprises use chaos engineering to test solutions with dummy failures and chaos. Since system failures have become much harder to predict, introducing chaos engineering can help prepare companies to withstand failures and come up with a preventive approach. This article will provide a complete walkthrough on chaos engineering, its techniques, and the companies that follow it. It will also dig deep into how enterprises use chaos engineering to stress-test AI-driven applications, the need for such tests, and AI experiments. Lastly, we will discuss the tools and best practices enterprises should follow to reap the maximum from AI solutions by employing chaos engineering.

What is Chaos Engineering?

Chaos engineering is the art and science of intentionally infiltrating flaws or noisy inputs into a system to test the resilience of the software or product. It is like a stress test. It helps identify all the failure attributes so that engineers and developers can rectify them before these faults cause outages or other disruptions. In this journey, the engineers study and carry out how a failure in the system can occur. Then, they work on the solutions by providing methodologies that help avoid them. Chaos engineers perform these stress tests in a controlled environment to identify potential points of failure in a system before they cause problems. Many enterprises like LinkedIn, Twilio, Netflix, Google, Microsoft, Facebook, and Amazon use chaos engineering to understand distributed architecture and microservices in systems.

Chaos Engineering in AI

Since AI solutions have become the new frontier differentiating legacy products from smarter ones, engineers should check their faults. Every AI solution is complicated, and AI models are unpredictable. Therefore, they invite unique flaws and challenges. To tackle this, chaos engineers inject controlled faults within an AI-powered system. Some common fault-injecting areas are dataset-based model training, inference APIs, machine learning pipelines, and decision automation frameworks. The primary objective of injecting faults and noisy inputs through chaos engineering in AI is to unveil weak points that could lead to biased outcomes, system outages, or degraded performance in AI applications.
In AI tests, chaos engineers create a simulated drift scenario in a chaos experiment by injecting synthetic or varied data to gauge how the model adapts or fails. Again, AI solutions run on powerful GPUs and High-Performance Computing (HPC) infrastructure. Chaos Engineering also extends its services into fault-tolerant checks for that. GPU contention, simulating node failures, or scalability bottlenecks allows enterprises to optimize for high availability in hardware performance and failover readiness.

Need for Chaos Engineering in AI-based Applications

AI applications and systems can be vulnerable to a wide variety of concerns. That is when AI engineers and developers decide to leverage chaos engineering. Let us explore the reasons why chaos engineering is essential in AI-based project development.

1. Shift in data distribution

AI engineers have to train most AI models with specific datasets. Shifting in the input data distribution can degrade the AI model performance. Chaos engineers perform stress tests on AI models by gradually modifying the datasets from similar to completely different.

2. Drift in AI model

AI-powered solutions need intensive model training over volumetric data. Once the AI solution goes live, it learns and modifies itself from user data. However, the performance of the AI model might degrade with the changing environment and user behavior. Chaos engineering tests the changing nature of the algorithm's environment and users' dynamic behavior by feeding synthetic data in the test environment.

3. Biases and fairness check

Unnoticed biases and unfair outcomes can lead to chaos and loss of business reputation. It also invites ethical concerns among users. Thus, AI developers need to address such issues. With chaos engineering, AI developers can check the outcome of the AI model by training it with biased datasets & then use these inferences to train the AI models, not to repeat it.

4. Adversarial change attacks

Another significant challenge AI products face is the threat that deep neural networks are susceptible to small and malicious data changes that lead to unexpected outcomes. Adulteration in input data (within a dataset) can cause extensive prediction errors. Chaos engineering can help mitigate that challenge by analyzing the type of errors and preventing them by bolstering algorithmic security.

Various Tools Used in AI Chaos Engineering

Numerous proprietary and open-source solutions are emerging that can help AI development companies support chaos engineering, determine chaos in systems, and fix them. The tools are as follows:

1. Chaos Toolkit

It offers extensible frameworks for devising, implementing, and investigating chaos experiments within AI solutions.

2. LitmusChaos

It is a Kubernetes-native chaos engineering tool. AI engineers can simulate application failures in cloud-based AI solutions.

3. Foolbox & CleverHans

These are libraries used for mimicking adversarial machine learning (ML) and identifying defects for rectification.

4. TensorFlow Model Analysis (TFMA)

It is beneficial to gauge AI model performance with varying slices of data.

5. Fault Injection Frameworks

It is another chaos engineering framework that can simulate infrastructure and application-level faults in software development projects.

6. Adversarial Robustness Toolbox (IBM)

It assists in testing and estimating the robustness of machine learning (ML) models by simulating faults and injecting false chaos in the models.

Best Practices and Guidelines for Chaos Engineering

Since chaos engineering is a discipline that uses simulated situations by injecting faults to uncover vulnerabilities, enterprises can use some best practices to maximize its benefits while minimizing risks.

1. Set a clear steady state

Before injecting chaos into a newly built application, enterprises should clearly define its steady state. The "steady state" is usually the app's standard behavior under ideal conditions. Initially, developers should measure diverse metrics like request success rate, system throughput, response time, or model accuracy as base indicators. Without a steady state and set of base indicators, it will not be possible to experiment with the AI solution using chaos engineering.

2. Take Small baby steps with control

Engineers should not introduce chaos at full scale and all at once. One should start small in the staging or post-development phase and simulate chaos with minor disruption. Then, a gradual increase in the complexity of chaos and noise can help enterprises understand the scope of the experiment. It can help identify the faults and chaos as the system's resilience grows.

3. Extensive monitoring

Monitoring is essential to understand where the chaos lies. Enterprises should leverage robust observability tools offering dashboards with metrics, logs, traces, and analytics to follow system behavior. Again, enabling real-time alerts can prevent chaos experiments from twisting into uncontrolled outages.

4. Learning culture is a must

Chaos engineering is a new form of experimenting with AI solutions and applications to stretch their potential by injecting pressure points and data for failure. Enterprises should consider failures during chaos tests as learning opportunities rather than blaming. Also, discovering how to implement chaos engineering differently with time should be a part of the learning curve.

Some Well-known Techniques for Chaos Engineering Experiments in AI

Let us now explore the experimental techniques that chaos engineers use to stress-test AI systems.

• Data noise injection: Engineers try to use adversarial perturbations or Gaussian noise to measure the deviation in model accuracy and changes in prediction.
• Data poisoning and label flipping: Another way to test AI systems is by simulating chaotic scenarios through mislabeled data to simulate how compromised datasets behave. It also helps assess the model's robustness.
• API Failure Simulation: Another way to simulate chaos is to check all integrated APIs with AI applications. Chaos engineers can mimic API outages or delayed responses. It also helps them observe how APIs upstream and downstream can handle missing or stale data.
• Input Distribution Shifting: Another way to inject chaos into AI systems is to feed the data model with data from diverse demographic or geographic regions. Then, engineers should observe the models' capability to generalize.
• Downgrading: Engineers also introduce chaos by replacing the current AI model with an older, less relevant version. It helps them monitor user experience and AI model outcomes.

Conclusion

We hope this article provided a complete understanding of chaos engineering, how AI engineers leverage it, its techniques, and best practices. In this AI-centric era, chaos engineering helps enterprises reach far beyond traditional software resilience. By intentionally introducing stress and faults within AI solutions and applications, enterprises can proactively uncover failure points, ensure robustness, and build trust in AI applications.
Contact us today for a consultation and discover how VE3 can help you in building trusted AI solution.