Q: Can I use my own dataset?

A:  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. For more information, see the deployment guide.

Q: Can I change the default instance types?

A:  Yes. By default, the solution uses an ml.t2.medium Amazon SageMaker notebook instance to orchestrate the model, and it uses an ml.3p.2xlarge Amazon SageMaker training instance to perform the training. But, you can modify the solution to use different instance types. For more information, see the deployment guide.

Q: Can I deploy this solution in any AWS Region?

A: Predictive Maintenance Using Machine Learning uses Amazon SageMaker which is currently available in specific AWS Regions only. Therefore, you must launch this solution in a region where Amazon SageMaker is available. For more information, see the deployment guide.

Training and Certification

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ML Building Blocks: Services and Terminology

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Demystifying AI/ML/DL

After taking this set of courses, you’ll understand how Artificial Intelligence (AI) led to Machine Learning (ML), which then led to Deep Learning (DL).

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AWS Certified Solutions Architect – Associate

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Partner resources

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Visit the following pages to learn more about the services we used to build this AWS Solution.

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