Catalog
concept#Machine Learning#DevOps#Data#Platform

MLOps

MLOps describes organizational practices and technical processes for production deployment, monitoring, and governance of machine learning models.

MLOps describes practices, processes and tools for operationalizing the deployment, monitoring, and governance of machine learning models in production.
Established
High

Classification

  • High
  • Organizational
  • Organizational
  • Intermediate

Technical context

Kubernetes and container orchestrationCI/CD systems (e.g. Jenkins, GitHub Actions)Feature and data registries (e.g. Feast, Delta Lake)

Principles & goals

Automate build, test, and deploy steps for ML artifacts.Versioning and traceability of data, models, and pipelines.Ensure monitoring, explainability, and governance in production.
Run
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Overautomation without quality controls leads to poor model quality.
  • Insufficient data governance can cause compliance risks.
  • Lack of observability hampers troubleshooting and trust.
  • Start with clearly prioritized models and iteratively expand the platform.
  • Consistently version data, models and pipelines.
  • Integrate monitoring and alerting from the beginning.

I/O & resources

  • Training data and metadata
  • Model code and experiments
  • Infrastructure and deployment templates
  • Versioned model artifacts and reproduction reports
  • Monitoring dashboards and alerts
  • Governance and audit logs

Description

MLOps describes practices, processes and tools for operationalizing the deployment, monitoring, and governance of machine learning models in production. It combines software engineering, data engineering, and DevOps principles to ensure reproducibility, automation, and continuous improvement. Focus is on end-to-end pipelines, monitoring, and lifecycle management.

✔

  • Faster and more stable deployment of models to production.
  • Improved reproducibility and traceability of experiments.
  • Early detection of performance and data issues in production.

✖

  • High initial effort for infrastructure and processes.
  • Complexity increases with the number of models and data sources.
  • Not all models justify extensive MLOps investments.

  • Model latency

    Average response time of a production model; important for user experience and SLAs.

  • Data and model drift rate

    Frequency and magnitude of distribution shifts in input data or model performance.

  • Pipeline lead time

    Time from code/data change to successful production deployment of a model.

Kubeflow in a data-driven platform

Kubeflow orchestrates training and deployment workflows in Kubernetes environments.

MLflow for experiment tracking and model registry

MLflow enables experiment traceability and a central model registry.

Google Cloud MLOps architecture for CI/CD

Architecture patterns for automated pipelines, testing, and governance in cloud environments.

1

Analyze existing processes and identify critical models.

2

Build a minimal end-to-end pipeline (data → training → deployment → monitoring).

3

Introduce automation stepwise, add quality gates and governance rules.

⚠️ Technical debt & bottlenecks

  • Ad-hoc scripts instead of standardized pipelines lead to maintenance burden.
  • Incomplete metadata hinders reproducibility.
  • Incompatible toolchains across teams complicate integration.
Data quality and availabilityInfrastructure cost and scalingCross-team coordination
  • Automated retraining without validation leads to performance regression.
  • Using production data for experiments without governance.
  • Treating all models with the same pipeline regardless of their requirements.
  • Underestimating effort for metadata and artifact management.
  • Neglecting security and compliance for model access.
  • Premature over-automation without stabilized processes.
Knowledge in machine learning and model evaluationSoftware engineering skills for CI/CD and infrastructure automationOperations and monitoring knowledge (observability)
Scalability of training and inference workflowsReproducibility and traceability of experimentsOperational monitoring, alerting and performance SLAs
  • • Compliance and data protection requirements can affect access and audit.
  • • Limited compute resources for large-scale training runs.
  • • Heterogeneous tool landscape across existing teams.