TensorFlow Extended vs Kubeflow
Developers should learn TFX when building scalable, reliable ML systems that require automated pipelines for continuous training and deployment meets developers should learn and use kubeflow when building and deploying ml pipelines in production, especially in cloud-native or hybrid environments where kubernetes is already in use. Here's our take.
TensorFlow Extended
Developers should learn TFX when building scalable, reliable ML systems that require automated pipelines for continuous training and deployment
TensorFlow Extended
Nice PickDevelopers should learn TFX when building scalable, reliable ML systems that require automated pipelines for continuous training and deployment
Pros
- +It is particularly useful for teams implementing MLOps practices, handling large datasets, or needing to maintain models in production with minimal manual intervention
- +Related to: tensorflow, machine-learning
Cons
- -Specific tradeoffs depend on your use case
Kubeflow
Developers should learn and use Kubeflow when building and deploying ML pipelines in production, especially in cloud-native or hybrid environments where Kubernetes is already in use
Pros
- +It is ideal for scenarios requiring scalable model training, automated ML workflows, and consistent deployment of ML applications, such as in large enterprises or research institutions handling complex data science projects
- +Related to: kubernetes, machine-learning
Cons
- -Specific tradeoffs depend on your use case
The Verdict
Use TensorFlow Extended if: You want it is particularly useful for teams implementing mlops practices, handling large datasets, or needing to maintain models in production with minimal manual intervention and can live with specific tradeoffs depend on your use case.
Use Kubeflow if: You prioritize it is ideal for scenarios requiring scalable model training, automated ml workflows, and consistent deployment of ml applications, such as in large enterprises or research institutions handling complex data science projects over what TensorFlow Extended offers.
Developers should learn TFX when building scalable, reliable ML systems that require automated pipelines for continuous training and deployment
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