.MP4, AVC, 380 kbps, 1920x1080 | AAC, 128 kbps, 2 Ch | 2h 57m | 620 MBGenre: eLearning | Language: EnglishConvert your Machine Learning project ideas into highly scalable solutions instantly with Amazon SageMakerThe 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 LearnBuild reliable, testable, and reproducible Machine Learning/Deep Learning workflows on SageMakerMigrate existing ML projects to SageMaker to minimize the time taken turning an idea into an actual model in productionData exploration and ML modeling on Jupyter Notebooks hosted on SageMakerTrain and deploy your custom Machine Learning/Deep Learning model on the cloud, via SageMakerConduct hyperparameter optimization on SageMaker in an easy and consistent wayEvaluate your models online by running A/B tests on SageMake