Hands-On Machine Learning Using Amazon SageMaker

The biggest challenge facing a Machine Learning professional is to train, tune, and deploy Machine Learning on the cloud. AWS SageMaker offers a powerful infrastructure to experiment with Machine Learning models. You probably have an existing ML project that uses TensorFlow, Keras, CNTK, scikit-learn, or some other library.  

This practical course will teach you to run your new or existing ML project on SageMaker. You will train, tune, and deploy your models in an easy and scalable manner by abstracting many low-level engineering tasks. You will see how to run experiments on SageMaker Jupyter notebooks and code training and prediction workflows by working on real-world ML problems.  

By the end of this course, you'll be proficient on using SageMaker for your Machine Learning applications, thus spending more time on modeling than engineering.   

Target Audience 

This course is designed for Machine Learning practitioners who have a working knowledge of Machine Learning and are keen to build, train, and deploy models on Amazon SageMaker.   

Business Outcomes 

• Train, evaluate, and deploy Machine Learning and Deep Learning models without the need to code custom solutions  

• Focus on real-world applications of Machine Learning and Deep Learning by leveraging SageMaker 

• Use SageMaker to build reproducible and testable Machine Learning workflows (training, offline evaluation, model versioning, model deployment, and A/B testing)