This learning path provides you with an introduction to the Amazon SageMaker service followed by the opportunity to practice using SageMaker in a series of practical hands-on labs.
- Understand the key services of Amazon SageMaker
- Learn how to use training data sets with machine learning models in SageMaker
- Understand how machine learning concepts can be applied to real-world scenarios
This learning path is intended for anyone who is:
- Interested in understanding how to deploy machine learning models on a managed service like Amazon SageMaker
- Looking to enrich their understanding of machine learning and how to use it to solve complex problems
- Looking to build a foundation for continued learning in the machine learning space and data science in general
To get the most out of this learning path, you should have a general understanding of data concepts as well as some familiarity with Amazon Web Services. Some experience in data or development is preferable but not essential.
If you have any feedback relating to this course, feel free to contact us at firstname.lastname@example.org.
Andrew is fanatical about helping business teams gain the maximum ROI possible from adopting, using, and optimizing Public Cloud Services. Having built 70+ Cloud Academy courses, Andrew has helped over 50,000 students master cloud computing by sharing the skills and experiences he gained during 20+ years leading digital teams in code and consulting. Before joining Cloud Academy, Andrew worked for AWS and for AWS technology partners Ooyala and Adobe.