Concepts in DevOps & Cloud, page 27
Argo Rollouts: Beyond Kubernetes Rolling Updates
Argo Rollouts replaces Kubernetes' basic RollingUpdate with safer, controlled strategies like canary and blue-green. It gradually shifts traffic to new versions while monitoring metrics, automating rollbacks if KPIs degrade.
Postmortem Report Template
A postmortem template is a standard structure for documenting an incident: summary, impact, timeline, root cause, what went well and poorly, and action items.
Cloud Computer Vision: Renting an AI's Eyes via API
Think of it as an API that lets your app 'see.' You send an image and get back structured data like object labels or text. It's used for content moderation, digitizing documents, or making photo libraries searchable.
Flux HelmRelease: Declarative Helm Charts in Git
A Flux HelmRelease is like putting your helm install command into a YAML file and checking it into Git. It lets you declaratively manage Helm charts for any application via GitOps.
Actionable Remediation Items
Actionable remediation items are specific, owned, trackable follow-ups from a postmortem that prevent recurrence or improve response. Good ones are concrete and assigned with a due date; vague items like be more careful are non-actionable and predictably…
OLM: Kubernetes' App Store for Operators
OLM is the app store for Kubernetes Operators: it installs, updates, and resolves dependencies declaratively. Use it when managing third-party or custom Operators across clusters.
MLOps: Applying DevOps to Machine Learning
MLOps applies DevOps principles to automate the machine learning lifecycle, creating an assembly line for models. It's for moving from notebooks to production systems that retrain automatically.
Flagger: Progressive Delivery for Kubernetes
Flagger is an automated traffic cop for Kubernetes releases. It gradually shifts traffic to new versions while monitoring metrics, enabling safe canary or blue/green deployments with service meshes or ingress controllers.
Counterfactual Reasoning in Incident Analysis
Counterfactual reasoning asks what would have prevented or mitigated an incident: if this alert had existed, if this check had run. Used well it finds systemic gaps, but it is a trap when it implies a single person should have just acted differently in…
controller-runtime: The Engine for Kubernetes Operators
Think of controller-runtime as the standard library for writing Kubernetes controllers. It handles the boilerplate of watching resources and reconciling state, forming the foundation for tools like Kubebuilder and Operator SDK.
Feature Store: The Single Source of Truth for ML
A feature store is the single source of truth for ML models, acting as a central kitchen for prepped ingredients (features). It's used to ensure the same feature logic is applied in both training and real-time inference, preventing model drift.
Argo CD Image Updater: Automate Image Updates
An Image Updater automates deployments by watching for new container image versions and telling Argo CD to update your app. It's used to automatically roll out new builds, but misconfiguring update strategies can accidentally deploy unstable tags to…
The Second Story of an Incident
The first story blames human error and stops there; the second story asks why the action made sense to the person at the time and what systemic conditions enabled it. Seeking the second story is the heart of blameless, learning-oriented incident analysis.
Hyperparameter Tuning for LLM Inference
Control an LLM's creativity versus predictability by tweaking its inference parameters. This is crucial for tasks like generating structured JSON versus creative text. The footgun is changing parameters without a clear goal, leading to chaotic output.
Argo CD ApplicationSet: Manage Many Applications as One
Think of an ApplicationSet as a factory for Argo CD Applications. It uses a template and generators to automatically create apps for many clusters or services in a monorepo.
Review of Reviews (Postmortem Metrics)
A meta-process that audits the quality and follow-through of postmortems themselves, tracking metrics like action-item completion, time-to-close, and recurrence of incidents.
ML Inference Endpoint: The API for Your Model
An ML inference endpoint is the stable API URL your application calls to get predictions. It separates the public URL from the underlying model, letting you swap models without changing client code.
Argo AnalysisTemplates: Reusable Health Checks for Deployments
An AnalysisTemplate is a reusable recipe for judging a deployment's health. During a canary rollout, Argo uses it to query metrics like error rates to decide whether to promote or roll back the new version.
Performance Profiling
Profiling measures where a program actually spends its time and resources, attributing CPU cycles, memory, or wall-clock latency to specific functions or call paths. It replaces guesswork with data so optimization effort targets the real bottleneck.
Kubernetes API Aggregation Layer: Extending the API Server
The API Aggregation Layer bolts custom API servers onto the main Kubernetes API, with kube-apiserver acting as a proxy. This powers features like the metrics server (kubectl top) and enables complex extensions.
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