USENIX | The Advanced Computing Systems Association

Continuous Training for Production {ML} in the {TensorFlow} Extended ({{{{{TFX}}}}}) Platform

Baylor D, Haas K, Katsiapis K, Leong S, Liu R, Menwald C, Miao H, Polyzotis N, Trott M, Zinkevich M. 2019. Continuous Training for Production {ML} in the {TensorFlow} Extended ({{{{{TFX}}}}}) Platform. 2019 USENIX Conference on Operational Machine Learning (OpML 19). :51--53.

Shooting the moving target: machine learning in cybersecurity

Arun A, Arnaldo I. 2019. Shooting the moving target: machine learning in cybersecurity. 2019 USENIX Conference on Operational Machine Learning (OpML 19). :13--14.

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Deep Learning Inference Service at Microsoft

Soifer J, Li J, Li M, Zhu J, Li Y, He Y, Zheng E, Oltean A, Mosyak M, Barnes C et al.. 2019. Deep Learning Inference Service at Microsoft. 2019 USENIX Conference on Operational Machine Learning (OpML 19). :15--17.

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Accelerating Large Scale Deep Learning Inference through {DeepCPU} at Microsoft

Zhang M, Rajbandari S, Wang W, Zheng E, Ruwase O, Rasley J, Li J, Wang J, He Y. 2019. Accelerating Large Scale Deep Learning Inference through {DeepCPU} at Microsoft. 2019 USENIX Conference on Operational Machine Learning (OpML 19). :5--7.

Using Amazon {SageMaker} to Operationalize Machine Learning

Venkateswar K. 2019. Using Amazon {SageMaker} to Operationalize Machine Learning.

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Nvidia {“NGC”} Deep Learning Containers

Tekur C, Gardiner F. 2019. Nvidia {“NGC”} Deep Learning Containers.

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Deep Learning Lifecycle Management with Kubernetes, {REST}, and Python

Tvaroska B. 2019. Deep Learning Lifecycle Management with Kubernetes, {REST}, and Python.

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{ModelOps} on {AWS}

Calhoun J. 2019. {ModelOps} on {AWS}.

Accelerating the Machine Learning Lifecycle with {MLflow}

[Anonymous]. 2019. Accelerating the Machine Learning Lifecycle with {MLflow}.

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Consistent {Multi-Cloud} {AI} Lifecycle Management with Kubeflow

Dutta D, Huang X. 2019. Consistent {Multi-Cloud} {AI} Lifecycle Management with Kubeflow.

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