by Pavlos Mitsoulis Ntompos | Publisher: Packt Publishing | Release Date: December 2018 | ISBN: 9781789530674

Convert your Machine Learning project ideas into highly scalable solutions instantly with Amazon SageMakerAbout This VideoTrain, evaluate, and deploy Machine Learning and Deep Learning models without the need to code custom solutionsFocus on real-world applications of Machine Learning and Deep Learning by leveraging SageMakerUse SageMaker to build reproducible and testable Machine Learning workflows (training, offline evaluation, model versioning, model deployment, and A/B testing)In DetailThe 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.The code bundle for this video course is available at- https://github.com/PacktPublishing/Hands-On-Machine-Learning-Using-Amazon-SageMaker-v-Downloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at http://www.PacktPub.com. If you purchased this course elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.

1. Chapter 1 : Your First Machine Learning Model on SageMaker
   • The Course Overview 00:02:44
   • AWS Setup 00:04:03
   • What Problem You Will Solve 00:01:26
   • Train the Model on SageMaker 00:16:56
   • Deploy the Model as a REST Service on SageMaker 00:08:53
2. Chapter 2 : Train Your Existing Machine Learning Model on SageMaker
   • Introduction 00:03:10
   • Train ML Model Locally 00:14:49
   • Enable Model Training on SageMaker 00:07:26
   • Train the Model on SageMaker 00:05:48
3. Chapter 3 : Deploy Your Existing Machine Learning Model on SageMaker
   • Deploy the Model Locally 00:02:53
   • Enable Model Deployment on SageMaker 00:07:44
   • Deploy the Model on SageMaker 00:06:37
4. Chapter 4 : Hyperparameter Optimization
   • Exploring Hyperparameter Optimization Methods 00:04:53
   • Hyperparameter Optimization in SageMaker 00:16:02
   • Tune Your Model 00:08:51
5. Chapter 5 : Offline and Online Model Evaluation
   • Why Online Evaluation 00:02:52
   • Build an Offline Evaluation Pipeline 00:04:18
   • Build an Online Evaluation Pipeline via A/B Testing on SageMaker 00:11:19
6. Chapter 6 : Natural Language Processing Application
   • NLP Problem Definition 00:09:37
   • Modeling Approach 00:06:43
   • Train and Evaluate NLP Model on SageMaker 00:18:51
   • Deploy NLP Model on SageMaker 00:11:17