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Docker for Machine Learning!
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Docker containers are a popular way to deploy ML environments that run consistently across various platforms. Data scientists are interested in pre-configured platforms that support docker images pre-installed with deep learning frameworks such as TensorFlow, Apache MXNet etc.
This presentation is about getting a quick high-level understanding of how to operationalize machine learning pipeline.
In this session, we will cover all of the basic topics of containers in the context of machine learning by:
* Taking a simple machine learning problem, building a model and containerize the application.* Setting up the docker environment with GPU support and download and run docker images pre-installed with TensorFlow framework
* Cisco Converged Infrastructure Solutions for AI/ML
Speakers: Haseeb Niazi, Paniraja Koppa
Track: AI, Machine Learning, HPC
Docker containers are a popular way to deploy ML environments that run consistently across various platforms. Data scientists are interested in pre-configured platforms that support docker images pre-installed with deep learning frameworks such as TensorFlow, Apache MXNet etc.
This presentation is about getting a quick high-level understanding of how to operationalize machine learning pipeline.
In this session, we will cover all of the basic topics of containers in the context of machine learning by:
* Taking a simple machine learning problem, building a model and containerize the application.* Setting up the docker environment with GPU support and download and run docker images pre-installed with TensorFlow framework
* Cisco Converged Infrastructure Solutions for AI/ML
Speakers: Haseeb Niazi, Paniraja Koppa
Track: AI, Machine Learning, HPC
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