SRE
SLI, SLO, and error budget are the vocabulary of reliability engineering. They give you a rigorous way to answer "is our service reliable enough?" and "how much risk can we take with this deployment?" without relying on gut feeling or incident retrospectives. This guide covers how
SRE
Synthetic monitoring runs scripted tests against your application continuously — from multiple geographic locations, using real browsers or API clients — to detect issues before users report them. Combined with canary deployments, it becomes your automated deployment safety net. Synthetic Monitoring Fundamentals Synthetic monitors are different from real user monitoring (RUM): Synthetic
SRE
When an incident happens at 3 AM, the quality of your response depends on two things: the quality of your runbooks and how many times your team has practiced them. Testing runbook automation and running incident drills converts theory into muscle memory. Automated Runbooks vs. Reference Runbooks Reference runbooks are
SRE
High availability is a promise — that your system will remain operational when components fail. Multi-region failover is how you keep that promise when an entire cloud region becomes unavailable. Testing your HA architecture requires deliberately causing the failures your system claims to handle. HA Architecture Patterns Active-passive (warm standby): Traffic
SRE
Chaos engineering is the practice of deliberately introducing failures into a system to discover weaknesses before they cause incidents. Netflix coined the term with Chaos Monkey — a tool that randomly terminates EC2 instances to force engineers to build resilient services. For Kubernetes workloads, Litmus Chaos has become the most widely
Machine Learning
scikit-learn pipelines combine preprocessing steps and models into a single object. They're elegant for training and inference, but they introduce testing challenges: how do you test a transformer in isolation? How do you verify that the pipeline doesn't leak information? How do you catch regressions when
Machine Learning
Feature stores solve the problem of feature reuse and training/serving consistency in ML systems. They also introduce a new category of bugs: features that compute correctly in isolation but produce wrong values when served at inference time due to point-in-time leakage, online/offline discrepancies, or stale data. Testing a
Machine Learning
A model you deploy today will degrade over time. The world changes — user behavior shifts, economic conditions fluctuate, upstream data systems evolve. The model doesn't know. Without drift monitoring, you find out when a user complains or a business metric tanks. Drift detection is the practice of monitoring
Machine Learning
A machine learning model that performs well on training data is meaningless. What matters is whether it generalizes to data it hasn't seen. Model validation is the discipline of measuring that generalization — reliably, without cheating. This guide covers the core validation techniques, when to use each, and the
Machine Learning
ML models fail silently when their input data changes. A column that was never null suddenly has 30% nulls. A categorical feature gains new values the model was never trained on. A numeric distribution shifts because a upstream system changed its calculation. Data quality testing catches these problems before they
SRE
Disaster recovery plans that exist only in documentation are not plans — they're aspirations. The difference between a DR plan that works and one that fails during an actual disaster is whether you've tested it. This guide covers the three tiers of DR testing: tabletop exercises, game
Machine Learning
Offline evaluation tells you a model performs better on held-out data. Production A/B testing tells you it actually improves the business metric you care about. These are different questions, and sometimes they have different answers. A new recommendation model might have higher offline AUC but lower click-through rate in