Machine Learning Specialist

  1. The need to scale ML models
  2. Design patterns for scalable ML applications
  3. Deploying ML models as services
  4. Running ML services in containers
    1. Docker
  5. Scaling ML services with Kubernetes
  6. ML services in production
  7. Conclusion