Q: Can I use my own dataset?
Yes. By default, the solution is configured to analyze a publicly available turbofan degradation simulation dataset from NASA. But, you can customize the solution to use your own dataset.
Q: Can I change the default instance types?
Yes. By default, the solution uses an ml.t2.medium Amazon SageMaker notebook instance to orchestrate the model, and it uses an ml.p3.2xlarge Amazon SageMaker training instance to perform the training. But, you can modify the solution to use different instance types.
Q: Can I deploy this solution in any Amazon Web Services Region?
Predictive Maintenance Using Machine Learning uses Amazon SageMaker which is currently available in specific Amazon Web Services Regions only. Therefore, you must launch this solution in a region where Amazon SageMaker is available.
Training and Certification
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Getting into the Serverless Mindset
This course will orient you to key serverless concepts to help you plan serverless architectures and applications. You will learn how serverless computing and its event-driven orientation influence your approach to application development, parallelization of tasks, and environment management.
Architecting on Amazon Web Services
This course shows you the fundamentals of building IT infrastructure on the Amazon Web Services platform. You learn how to optimize the Amazon Web Services Cloud by understanding Amazon Web Services services and how they fit into cloud-based solutions.
Amazon Web Services Certified Advanced Networking – Specialty
This exam tests your technical expertise in designing and implementing Amazon Web Services and hybrid IT architectures at scale. This is for anyone who performs complex networking tasks.
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