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AWS Summit Online Australia & New Zealand 2021

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Scaling through distributed training

Machine learning data sets and models continue to increase in size, bringing accuracy improvements in computer vision and natural language processing tasks. This means data scientists will increasingly encounter situations where their model training cannot fit on one GPU instance. Distributed training enables scale beyond the limitations of one GPU, either through data parallelisation or model parallelisation. In this session, learn the basic concepts behind distributed training and understand how Amazon SageMaker can help you implement distributed training for your models faster.
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A/B testing machine learning models with Amazon SageMaker MLOps
A/B testing machine learning models with Amazon SageMaker MLOps

Machine learning operations (MLOps) enable developers and scientists to improve their models for various re...

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Using reinforcement learning to solve business problems
Using reinforcement learning to solve business problems

Join this session to learn how to structure your business problem into a reinforcement learning structure, ...