Hands-On Machine Learning Using Amazon SageMaker [Video]

More Information
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
About

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.

The code bundle for this video course is available at- https://github.com/PacktPublishing/Hands-On-Machine-Learning-Using-Amazon-SageMaker-v-

Style and Approach

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.

Features
  • 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)
Course Length 2 hours 57 minutes
ISBN 9781789530674
Date Of Publication 30 Dec 2018

Authors

Pavlos Mitsoulis Ntompos

Pavlos Mitsoulis Ntompos has 7 years of machine learning and software engineering experience. Currently, he is a staff software engineer (machine learning) at HomeAway (an Expedia Group brand), leading machine learning initiatives to support growth marketing. Additionally, he is the creator of Sagify, an open source library to simplify the training, evaluation, and deployment of machine learning models to SageMaker.

In the past, he used to be an instructor at the MSc in Business Analytics offered by Athens University of Economics and Business, teaching applications of machine learning using big data technologies. He has a master’s degree in Computer Science from Imperial College London. Finally, Pavlos always seeks to apply and discover new machine learning theories and best practices.