Hands-On Machine Learning Using Amazon SageMaker [Video]
This is the code repository for Hands-On Machine Learning Using Amazon SageMaker [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
About the Video Course
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
Instructions and Navigation
Assumed Knowledge
To fully benefit from the coverage included in this course, you will need:
A working knowledge of Machine Learning and for practitioners who are keen to build, train, and deploy models on Amazon SageMaker.
Technical Requirements
This course has the following software requirements:
Minimum Hardware Requirements:
For successful completion of this course, students will require the computer systems with at least the following:
OS: MacOS or Linux Processor: 1.8GHz dual-core Intel Core i5 Memory: 8GB Storage: 128GB
Recommended Hardware Requirements:
For an optimal experience with hands-on labs and other practical activities, we recommend the following configuration:
OS: MacOS or Linux Processor: 2.3GHz quad-core Intel Core i5 Memory: 16GB Storage: 128GB
Software Requirements
Browser: Chrome, Firefox, Safari
PyCharm Community Edition: https://www.jetbrains.com/pycharm/download
Python 3.6 (MacOS: Install via homebrew with command: brew install https://raw.githubusercontent.com/Homebrew/homebrew-core/f2a764ef944b1080be64bd88dca9a1d80130c558/Formula/python.rb, Linux: https://docs.python-guide.org/starting/install3/linux/)
Anaconda (MacOS: https://conda.io/docs/user-guide/install/macos.html, Linux: https://conda.io/docs/user-guide/install/linux.html)