This is the second of two articles. Read also: Why You Should Break Your ML Pipelines on PurposeIn Part One of this series, we made the case for using chaos engineering to enhance the reliability of machine learning (ML) pipelines. The heart of any successful ML operation is its infrastructure — the data pipelines, model registries and feature stores. These are the components most susceptible to the kinds of failures that chaos engineering is designed to expose.Once you’re aware of the most common failure modes in ML pipelines, the next question is: How do you safely simulate these failures to test your system’s resilience?The answer lies in applying chaos engineering principles to the unique components of the ML life cycle: data pipelines, model registries and feature stores. These aren’t your typical application dependencies — they’re tightly coupled to the logic of your AI system. If any of them silently fail, your model can degrade in ways that traditional monitoring simply won’t catch.Let’s walk through how to inject chaos into each component and why it matters.Injecting Chaos Into Data PipelinesData pipelines are the lifeblood of ML systems. They move raw data from source systems to the feature engineering and training stages. But pipelines are often complex directed acyclic graphs (DAGs) with multiple failure points: flaky APIs, broken cron jobs, slow ingestion or format shifts.Failure scenarios to simulate:Data is delayed or arrives out of order.File formats change silently (such as CSV to JSON).Missing values increase unexpectedly.Entire columns or tables are dropped.Chaos injection techniques:File delay simulation: Temporarily hold back a daily ingestion file in your staging environment. Use sleep delays in Airflow/Kubeflow to simulate cron job latency.Schema drift: Inject a version of a data set with a renamed or missing column to see how your extract, transform, load (ETL) scripts or feature store reacts.API error simulation: Replace live API calls with mocks that randomly return 500, 429 or malformed data.Introduce partial data: Use Chaos Mesh to kill the middle of a multistage ETL job and test whether downstream logic detects and reports incomplete data.Tools to use:Chaos Mesh and Python/Bash scripts.Airflow task retries and failure simulation.Great Expectations for post-ingestion validation.Injecting Chaos Into Feature StoresFeature stores serve as the bridge between training and serving. They’re expected to provide consistent, fresh and versioned features to both environments. But they’re also vulnerable to staleness, format drift and low observability.Failure scenarios to simulate:A batch job fails, and features aren’t updated.A real-time stream lags behind by hours.Feature version mismatch between training and inference.Feature distribution changes (mean, std deviation) over time.Chaos injection techniques:Disable a feature update job (or simulate it with a Chaos Mesh pod kill) and measure how downstream models behave with stale features.Serve corrupted features by injecting out-of-range values (such as very large numbers for normalized fields) to test model robustness.Simulate skew by introducing different feature distributions at training versus inference time (apply a shift or transformation during serving only).Test fallback logic by removing a commonly used feature and observing whether the system defaults to another or fails entirely.Tools to use:Feast, a popular open source feature store, with Chaos Mesh to kill online store update processes.Custom scripts to swap .parquet or .csv files with corrupted ones.Great Expectations to validate feature consistency.Injecting Chaos Into Model RegistriesModel registries like MLflow, SageMaker Model Registry or custom artifact stores are central to tracking, versioning and deploying models. A broken registry or mismatched metadata can result in serving the wrong model, losing traceability or invalid rollbacks.Failure scenarios to simulate:An old model version is accidentally redeployed.A new model is registered without associated metadata (input schema).The model signature has changed, but inference code hasn’t.The registry is unreachable at deployment time.Chaos injection techniques:Overwrite version tags to point to incorrect artifacts and test downstream consumers for compatibility checks.Remove or scramble metadata (expected feature list, model type) and verify whether your CI/CD pipeline validates models before serving.Block access to the registry using a network fault or firewall rule to simulate an outage during deployment.Deploy a broken model intentionally to staging and measure alerting, rollback and serving behavior.Tools to use:MLflow APIs and command-line interface (CLI) to simulate bad registrations.Chaos Mesh (network chaos) to block registry access.Seldon Core or custom CI/CD logic to test deployment guardrails.Tools for Injecting Chaos Into ML PipelinesInjecting chaos into an ML pipeline isn’t just about flipping random switches. It’s about strategically simulating real-world failure modes to test how your system behaves under stress. To do this well, you need the right tools.Chaos testing in ML requires blending infrastructure-level fault injection tools with ML-specific data and model validation frameworks. The goal is to simulate failure across the full life cycle — from raw data ingestion to real-time inference — without harming your production systems.Here are some of the most effective tools to help you design and execute chaos experiments tailored for AI systems:Chaos MeshBest for: Injecting infrastructure-level faults into Kubernetes-based ML platforms (Kubeflow, MLflow, Airflow on K8s)Chaos Mesh is a Kubernetes-native chaos engineering framework that enables you to simulate various types of failures — such as pod failure, network latency, disk corruption and even time skew — directly inside your ML infrastructure.LitmusChaosBest for: Creating chaos workflows across environments and services, including non-Kubernetes systems.LitmusChaos is another Cloud Native Computing Foundation (CNCF) project with strong support for complex, multistep chaos scenarios. While Chaos Mesh excels at targeted K8s faults, LitmusChaos is better suited for choreographing full chaos workflows. It’s especially useful in multicloud or hybrid MLOps stacks.Great ExpectationsBest for: Validating data integrity, expectations and detecting subtle data quality regressions.Great Expectations is a data validation framework, but in chaos engineering for ML, it serves a crucial role: detecting invisible data failures. It ensures that your input data conforms to expected patterns and schema definitions, even after a failure is introduced upstream.Seldon CoreBest for: Robust model serving, canary deployments, versioning and inference failover.Seldon Core is a Kubernetes-native model serving framework that offers A/B testing, traffic splitting, rollback mechanisms and detailed metrics for real-time model behavior. For chaos experiments, it enables model version switching, inference fault injection and monitoring at scale.MLflowBest for: Model experiment tracking, versioning and registry management.MLflow isn’t a chaos engineering tool by design, but it plays a critical role in managing and auditing chaos experiments, especially in model versioning and evaluation. You can use it to track performance degradation across experiments, identify regressions and enforce deployment rules.Bonus: Custom Python and Bash ScriptsBest for: Lightweight, targeted chaos experiments in ETL and training pipelines.Sometimes, simple tools do the trick. For injecting chaos into data transformation scripts, notebook-based training jobs or CI/CD workflows, scripting with Python or Bash gives you full control. These are especially useful in Airflow DAGs or Kubeflow Pipelines, where you want to test failures mid-task.Break To Build BetterIn DevOps, we’ve long understood that systems don’t become resilient by chance; they become resilient because we test them in the worst possible conditions. Chaos engineering has taught us to simulate network outages, kill services at random and stress-test environments not to cause destruction, but to uncover the invisible cracks that would eventually break us.It’s time we brought that same philosophy to ML systems.ML pipelines are different. They don’t throw exceptions when something breaks. They degrade — quietly, dangerously and often without detection. A slight delay in feature delivery, a mislabeled training batch or an unnoticed shift in input distributions can corrupt the behavior of your models for days or weeks without ever tripping a monitor or firing an alert.The real danger isn’t downtime; it’s being confidently wrong.That’s why AI systems need more than high availability. They need:Observability: Monitoring not just logs and latency, but data quality, feature drift and prediction distributions.Fault tolerance: The ability to degrade gracefully, fall back safely and trigger intelligent alerts when things go wrong.Chaos readiness: Systems that are intentionally tested under failure conditions, so when failure comes (and it will), you already know what breaks and how to recover.Chaos engineering is the missing feedback loop in most MLOps stacks.By injecting controlled failure into data ingestion, feature processing, training, serving and feedback loops, you move from reactive firefighting to proactive resilience building. You stop hoping your pipeline works, and start knowing how it fails — and how it heals.What You Can Do NextHere’s how to get started with chaos engineering for your ML systems today:Pick one pipeline component — feature ingestion, training or serving — and inject a simple fault (such as delay, schema mismatch or missing column).Measure the impact: Track model accuracy, latency, alerting and business KPIs.Document your findings: What broke? What didn’t? What needs to be more robust?Share it with your team: Use it as a foundation to start integrating chaos tests into CI/CD pipelines.Iterate: Expand your chaos tests to new components. Automate. Schedule. Monitor.The post A Guide To Stress-Testing Your ML Data Pipelines appeared first on The New Stack.