Drift
Drift is a change over time in a system’s behavior, state, data, or configuration relative to a reference or expected baseline.
Machine Learning Platform Engineering
Data drift is a change over time in the statistical distribution or characteristics of input data relative to the data used to develop a model.
- How it is used
- Flags changes in production data that may threaten prediction quality and require investigation or maintenance.
- How deep it goes
- Develops report generation, dashboarding, and batch and API integration, while leaving microbatch size dependent on the application.
- Examples
- Uses income-classifier reports, altered image brightness, and changed movie ratings and item factors.
- What you can do after
- Apply it via Evidently or deepchecks to compare reference and current data, and integrate checks into batch or API monitoring.
Read how Machine Learning Platform Engineering explains drift →
Machine Learning System Design
Model drift is a change over time in a machine learning model’s behavior or performance caused by changes in its inputs, relationships, or operating environment.
- How it is used
- Frames the production task of keeping predictions relevant as inputs and real-world relationships change.
- How deep it goes
- Defines data and concept drift and outlines monitoring and response choices, without detailing every possible drift scenario.
- Examples
- Uses a new marketplace customer segment and an inventory prediction system with time-series and previous-week-sales fallbacks.
- What you can do after
- Diagnose/debug with it by checking quality, comparing inputs and performance, then choosing model updates or a fallback.
Statistics Every Programmer Needs
Nonstationary behavior is temporal behavior whose statistical properties, such as level or variability, change over time.
- How it is used
- Flags changing time-series behavior that must be assessed before fitting a stationary-data forecasting model.
- How deep it goes
- Works through chronological splitting, visual inspection, an ADF test, first-order differencing, and post-transformation checks without establishing a universal remedy.
- Examples
- Uses Apple closing prices, hand-calculated daily differences, Python transformations, lag plots, and ADF p-values.
- What you can do after
- Diagnose/debug with it by checking a training series for nonstationarity and reassessing it after differencing.
Effective Platform Engineering
Configuration drift is the divergence between an infrastructure system's actual state and its declared or desired configuration.
- How it is used
- Uses drift detection to keep deployed infrastructure aligned with intended configuration after releases and between pipeline runs.
- How deep it goes
- Explains manual-change causes and recurring configuration tests, but stops at detection rather than specifying automated repair.
- Examples
- Uses a console change to illustrate drift and an Awspec IAM role check to illustrate configuration testing.
- What you can do after
- Diagnose/debug with it by running recurring checks that compare deployed settings with required settings and flag unexpected changes.
Automating API Delivery
Configuration drift is a divergence between a system's desired configuration and its actual deployed configuration.
- How it is used
- Identifies mismatches that API gateway deployment agents must detect and reconcile against repository-defined configuration.
- How deep it goes
- Explains causes, comparison, alerts, and correction of configuration drift, without specifying an agent implementation or command sequence.
- Examples
- Uses a direct API gateway UI change and a live API response missing a mandatory documented field.
- What you can do after
- Operate/configure API gateway changes through Git and use reconciliation to detect and correct deployed configuration drift.
Terraform in Depth: Infrastructure as Code with Terraform and OpenTofu
Drift is a discrepancy between the infrastructure or files Terraform expects and the resources or files actually present.
- How it is used
- Identifies mismatches so Terraform plans and deployment workflows can decide whether to restore configuration, accept a change, or ignore an attribute.
- How deep it goes
- Explains refresh-based detection, cause investigation, and responses to expected changes; does not detail the four referenced error categories.
- Examples
- Uses externally added tags, autoscaling counts, database upgrades, regenerated values, and local files that disappear between runs.
- What you can do after
- Diagnose/debug with Terraform plans and refresh-only mode, then investigate the cause before choosing reconciliation, acceptance, or ignore_changes.
Read how Terraform in Depth: Infrastructure as Code with Terraform and OpenTofu explains drift →
Platform Engineering on Kubernetes
Configuration drift is the divergence between the declared configuration of a system and the configuration actually running in that system.
- How it is used
- Controls Kubernetes environment changes by making versioned Git configuration the desired state.
- How deep it goes
- Explains detection, reconciliation, validation, and rollback, but does not detail the internal implementation of reconciliation.
- Examples
- Uses Git commits, Kubernetes clusters, Helm configuration, kubectl application, and Argo CD reconciliation to illustrate drift.
- What you can do after
- Diagnose drift by comparing live workloads with Git, reconcile differences, validate deployments, and roll back through earlier commits.
Read how Platform Engineering on Kubernetes explains drift →
Delivering Machine Learning Projects: From design to deployment
Model drift is a change in the data, relationships, or operating conditions underlying a machine learning model that can reduce its suitability or performance over time.
- How it is used
- Treats drift as a post-deployment signal to review model suitability and consider repair or retraining.
- How deep it goes
- Distinguishes weak original evaluation from changing relationships and noise, then outlines retraining inputs and coordination.
- Examples
- Uses changing demand for smart trousers after a shift to working from home, and changing data noise.
- What you can do after
- Diagnose drift by reviewing prior testing, then plan retraining with updated data, a test regime, and stakeholder buy-in.
Read how Delivering Machine Learning Projects: From design to deployment explains drift →
LLMs in Production
Data drift is a change over time in the statistical properties or distribution of data presented to a machine-learning system.
- How it is used
- Uses monitoring to catch data changes associated with poor ML or LLM performance and guide adjustment or retraining.
- How deep it goes
- Lists missing values, uniqueness, and standard-deviation shifts as signals, without detailing a drift-detection algorithm.
- Examples
- Uses fake reviews manipulating recommendations and highlights the difficulty of assessing drift in text data.
- What you can do after
- Diagnose silent model degradation by monitoring data and respond by adjusting the model or retraining it.
Digital Twins in Action
Data drift is a change in the distribution of input data relative to the reference data used to train a model.
- How it is used
- Flags changing assumptions in production digital twins so operators can review shifts rather than rely on silently outdated predictions.
- How deep it goes
- Distinguishes three drift types and demonstrates a single-feature report, while leaving multidimensional sensor monitoring as an extension.
- Examples
- Uses changing solar production, an induction-cooktop replacement, sensor miscalibration, and a reference-versus-current power-data report.
- What you can do after
- Apply it via Evidently to compare reference and current telemetry, generate an HTML report, and bring changes to an operator for review.
Kafka for Architects
Configuration drift is the divergence between a system's actual configuration and its declared desired configuration.
- How it is used
- Detects discrepancies between Kafka cluster configuration and its Git-declared desired state.
- How deep it goes
- Defines drift and describes detection, warnings, and corrective suggestions, but notes that authorized direct changes cannot be prevented.
- Examples
- Uses an authorized user creating a topic directly as an example of a change that can cause drift.
- What you can do after
- Operate Kafka configuration through Git-driven comparison, reconciliation, and periodic drift validation.
Bootstrapping Microservices, Second Edition: With Docker, Kubernetes, GitHub Actions, and Terraform
Configuration drift is the divergence between the recorded or declared configuration of infrastructure and its actual state.
- How it is used
- Uses Terraform state refresh and configuration comparison to detect and reconcile infrastructure changed outside declared code.
- How deep it goes
- Explains ownership, state refresh, and reconciliation while stopping short of a detailed remediation workflow.
- Examples
- Uses manually tweaked infrastructure and an Azure resource group recorded in terraform.tfstate as concrete examples.
- What you can do after
- Diagnose drift by inspecting Terraform state and understanding how refresh compares live resources with declared configuration.
Related concepts
- concept driftincludes · Concept drift is a change over time in the data-generating process, including the distribution of inputs or the relationship between inputs and target outcomes.
- reconciliationaddressed by · Reconciliation is the process of comparing related states, records, or outputs to identify differences and, when needed, resolve them or bring them into agreement.
- monitoringaddressed by · Monitoring is the ongoing observation and measurement of a system’s behavior, state, or operational signals to assess its condition and detect changes or problems.
- infrastructure as codeaddressed by · Infrastructure as code is the practice of defining, provisioning, and managing computing infrastructure through machine-readable code or configuration rather than manual procedures.
- Terraformaddressed by · Terraform is an open-source infrastructure-as-code tool for defining, provisioning, and managing infrastructure resources through declarative configuration.
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