<p>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.</p> <p>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.<br />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.</p> <p>The code bundle for this video course is available at-&nbsp;<a href="https://github.com/PacktPublishing/Hands-On-Machine-Learning-Using-Amazon-SageMaker-v-" target="_blank">https://github.com/PacktPublishing/Hands-On-Machine-Learning-Using-Amazon-SageMaker-v-</a></p> <h1>Style and Approach</h1> <p>Using realistic examples, this hands-on course will show you how to run your existing or new Machine Learning pipelines on SageMaker. More specifically, the step-by-step instructions will help you to train, deploy, and evaluate your Machine Learning/Deep Learning models on SageMaker.</p>