MLOps
CI/CD
ensures consistent integration of ML code and automated deployment into production.
Canary &
A/B Testing
continuously validates the performance and reliability of ML models.
Monitor & Logging
this includes tracking the input data, the predictions made, and any workloads.
Versioning
involves tracking and managing different versions of ML models, similar to how software versions are managed.
Scaling
ensure that ML models can be scaled up efficiently to handle increased workload or data size.