About This 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.
The code bundle for this video course is available at- https://github.com/PacktPublishing/Hands-On-Machine-Learning-Using-Amazon-SageMaker-v-
- Certificate will provided in this course on Completion
- Full lifetime access
- Available on Mobile & Laptop
What Students Will Learn In Your Course?
- 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 SageMaker
Are There Any Course Requirements Or Prerequisites?
This course is designed for Machine Learning practitioners who have a working knowledge of Machine Learning and are keen to build, train, and deploy models on Amazon SageMaker.
Who Are Your Target Students?
- 22 lectures