Convert your Machine Learning project ideas into highly scalable solutions instantly with 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.

What You Will Learn

Build reliable, testable, and reproducible Machine Learning/Deep Learning workflows on SageMaker
Migrate existing ML projects to SageMaker to minimize the time taken turning an idea into an actual model in production
Data exploration and ML modeling on Jupyter Notebooks hosted on SageMaker
Train and deploy your custom Machine Learning/Deep Learning model on the cloud, via SageMaker
Conduct hyperparameter optimization on SageMaker in an easy and consistent way
Evaluate your models online by running A/B tests on SageMake

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