Build, train, and deploy predictive maintenance models at industrial scale (sponsored by Amazon Web Services)
Across a wide spectrum of industries including oil and gas, agriculture, and manufacturing, customers are starting to use prediction maintenance models to proactively adjust processes and equipment in order to fix problems before they impact production. The result is an optimized supply chain and improved working conditions.
Sunil Mallya explores how to use data from equipment to build, train, and deploy predictive models. You’ll dive deep into the architecture, deployment guide, and development resources for using the turbofan degradation simulation dataset to train the model to recognize potential equipment failures. And you’ll explore how to automate the detection of potential equipment failures and how to provide recommended actions. Sunil walks you through an AWS CloudFormation for predictive maintenance so you can get started quickly.
This session is sponsored by Amazon Web Services.
What you'll learn
- Learn how to use prediction maintenance to stop problems before they start
Amazon Web Services
Sunil Mallya is a Principal Deep Learning Scientist at Amazon Web Services.
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