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.
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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.