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
- AWS Certified Machine Learning Specialty (MLS-C01)
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 covers all aspect of AWS Certified Machine Learning Specialty (MLS-C01)
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:
AWS Certified Machine Learning Specialty (MLS-C01)
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
