Wednesday, October 28, 2020
Machine and deep learning become essential for a lot of companies for internal and external use. One of the main issues with its deployment is finding the right way to train and operationalize the model within the company. Serverless approach for deep learning provides simple, scalable, affordable and reliable architecture for it. My presentation will show how to do so within AWS infrastructure.
Serverless architecture changes the rules of the game - instead of thinking about cluster management, scalability, and queue processing, you can now focus entirely on training the model. The downside within this approach is that you have to keep in mind certain limitations and how to organize training and deployment of your model in a right fashion.
I will show how to deploy train and inference pipelines for Tensorflow models on serverless AWS infrastructure.
My talk will be beneficial for machine learning engineers and data scientists.
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