Statistical data analysis involves multiple stages of data preprocessing, analysis, modeling, training, parameter optimization, testing and serving. In this talk, Alex will discuss lessons learned from AWS SageMaker, an integrated framework for handling all stages of analysis. AWS uses open source components such as Jupyter, Docker containers, Python and well established deep learning frameworks such as Apache MxNet and TensorFlow for an easy to learn workflow. At the same time, flexibility to integrate third party code and management is vital for real world deployment.
Alex Smola is director of machine learning at Amazon.
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