AWS SageMaker Complete Course| PyTorch & Tensorflow in NLP

Build DL/ML model in Sklearn,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.

What you’ll learn

  • What is SageMaker and Why it is required.
  • SageMaker Architechure.
  • Model Building using existing Docker Image in SageMaker.
  • Model Building using existing algorithm in SageMaker.
  • Model Building using SageMaker Pre-built algorithms.
  • Model Building in Tensorflow/Keras.
  • Model Building in Pytorch.
  • How to deploy the models in SageMaker.
  • How to make predictions from Endpoints.
  • Create complete End-to End machine learning Pipeline Workflow.
  • Real time example of NLP.
  • How to schedule the SageMaker notebook for Retraining.
  • How to Build ,deploy and schedule the Model.

Course Content

  • Amazon SageMaker: Introduction –> 5 lectures • 25min.
  • Bring your own (Any) Tensorflow/Keras Docker container to SageMaker –> 8 lectures • 1hr 4min.
  • Bring your own (Any) PyTorch BERT Docker Container –> 7 lectures • 56min.
  • Bring Your own(Any) Algorithm in SageMaker –> 7 lectures • 1hr 17min.
  • SageMaker Pre-built Algorithm –> 5 lectures • 46min.
  • SageMaker Pre-built Algorithm: LogisticRegression –> 2 lectures • 14min.
  • Model Deploy and Making Predictions –> 2 lectures • 8min.
  • Create Pipeline, Workflow and schedule the SageMaker Notebook –> 5 lectures • 39min.

AWS SageMaker Complete Course| PyTorch & Tensorflow in NLP

Requirements

  • Free or paid subscription to AWS is required. It may ask for Phone and/or Credit Card for verification.
  • Python Basic knowledge.

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