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Build deep learning model in Tensorflow/Keras & PyTorch. How to bring docker container&Algorithm from local to Sagemaker


This course is complete guide of AWS SageMaker wherein student will learn how to build, deploy SageMaker models by brining on-premises docker container and integrate it to SageMaker. Course will also do deep drive on how to bring your own algorithms in AWS SageMaker Environment. Course will also explain how to use pre-built optimized SageMaker Algorithm.


Course will also do deep drive how to create pipeline and workflow so model could be retrained and scheduled automatically.


This course will give you fair ideas of how to build Transformer framework in Keras for multi class classification use cases. Another way of solving multi class classification by using pre-trained model like Bert .


Both the Deep learning model later encapsulated in Docker in local machine and then later push back to AWS ECR repository.


This course offers:


What is SageMaker and why it is required


SageMaker Machine Learning lifecycle


SageMaker Architecture


SageMaker training techniques:


Bring your own docker container from on premise to SageMaker


Bring your own algorithms from local machine to SageMaker


SageMaker Pre built Algorithm


SageMaker Pipeline development


Schedule the SageMaker Training notebook


More than 5 hour course are provided which helps beginners to excel in SageMaker and will be well versed with build, train and deploy the models in SageMaker




Who this course is for:

Data Engineers or data scientist

Developers who want to start a career in or wants to learn about the exciting domain of Data Science and Machine Learning

Business Analysts who want to apply Data Science to solve business problems

Learn how to build train and deploy it in AWS cloud

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