AI & Machine Learning

MLOps

The practice of applying DevOps principles to machine learning systems, encompassing model development, deployment, monitoring, and lifecycle management.

Also known as:ML EngineeringMachine Learning Operations

What is MLOps?

MLOps (Machine Learning Operations) is a set of practices that combines machine learning, DevOps, and data engineering to deploy and maintain ML systems in production reliably and efficiently. It addresses the unique challenges of ML lifecycle management.

MLOps Lifecycle

1. Data Management

  • Data collection
  • Data versioning
  • Feature stores

2. Model Development

  • Experimentation
  • Training
  • Validation

3. Model Deployment

  • Packaging
  • Serving
  • Scaling

4. Monitoring

  • Performance tracking
  • Drift detection
  • Alerting

5. Governance

  • Model registry
  • Audit trails
  • Compliance

Key Components

Version Control

  • Code, data, models
  • Reproducibility
  • Experiment tracking

CI/CD for ML

  • Automated testing
  • Model validation
  • Deployment pipelines

Feature Store

  • Centralized features
  • Consistency
  • Reusability

Model Registry

  • Model versioning
  • Metadata
  • Lifecycle management

Challenges

  • Data dependencies
  • Model decay
  • Reproducibility
  • Testing complexity
  • Team coordination

Tools

Platforms

  • MLflow
  • Kubeflow
  • Weights & Biases
  • SageMaker

Serving

  • TensorFlow Serving
  • Triton
  • Seldon
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