The 7 Pillars of MLOps
Modern machine learning deployment requires more than just code - it's an engineering discipline that ensures models survive the journey from research to production. Our latest whitepaper outlines the key components of production-grade ML systems.
- Version-controlled infrastructure as code
- Real-time monitoring for data drift detection
- Scalable inference endpoints with auto-scaling
- Comprehensive A/B testing frameworks
CI/cd for ML Systems
Continuous integration and deployment pipelines are crucial for ensuring model updates are tested and validated before deployment. Our automated pipelines integrate unit tests, integration testing, and canary rollouts to ensure zero-downtime deployments.
# Continuous Deployment Pipeline
stages:
- validate
- train
- test
- deploy
validation:
script: python validate_data.py
training:
script:
- python train.py
- python evaluate.py
deployment:
only_on_success: true
script: docker push model:v1.2.3