Serverless architecture for AI applications
Who is this presentation for?Data engineers, data architects, developers
Machine learning (ML) and deep learning (DL) are becoming more and more essential for businesses for internal and external use; one of the main issues with deployment is finding the right way to train and operationalize the model. A serverless approach for deep learning provides cheap, simple, scalable, and reliable architecture for it. Rustem Feyzkhanov digs into how to do so within AWS infrastructure.
Serverless architecture changes the rules of the game—instead of thinking about cluster management, scalability, and query processing, you can focus completely on training the model. The downside of this approach is that you have to keep in mind certain limitations and how to correctly organize the training and deployment of your model.
You’ll learn how to train and deploy TensorFlow and PyTorch models on AWS infrastructure and how you can easily use pretrained models for your tasks. The AWS Function-as-a-Service solution—Lambda—can achieve very significant results, such as 20–30K runs per one dollar (completely pay-as-you-go model), 10K functions can be run in parallel, and it easily integrates with other AWS services. It allows you to easily connect it to an API, chatbot, database, or stream of events.
- A basic understanding of AWS, TensorFlow, and PyTorch
What you'll learn
- Learn how to architect serverless ML and DL in your cloud pipeline and if ML and DL serverless is right for you and your company
- Identify hidden problems and tricks when using serverless DL and ML
Rustem Feyzkhanov is a machine learning engineer at Instrumental, where he creates analytical models for the manufacturing industry. Rustem is passionate about serverless infrastructure (and AI deployments on it) and is the author of the course and book Serverless Deep Learning with TensorFlow and AWS Lambda.
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