Abstract: 

Realizing when mechanical devices (e.g. pump data, manufacturing devices) are having mechanical issues and are showing signs of failure (via sensors) are important items to know when you need to keep your mechanical devices running 24/7. Therefore creating failure prediction algorithms and/or model(s) are essential parts of your mechanical equipment maintenance toolbox. But how do you get started? Is it hard to curate sensor data? How do you create, train and deploy failure prediction algorithms and models? What kind of platform do you use for these items?

Failure prediction in real time on time series data (gathered by sensors) can be realized with the use of Open Source tools. We will deliver an overall view of how to start with the generation of new raw sensor data (typically captured by an Edge device), create a failure prediction model and end up with a real time graph that shows alerts warning that a failure is imminent for a mechanical device.

But that’s not where the story ends. We also need to address ‘security’. From transferring and storing data to working with models, security is vital to working with sensors at the Edge. Taking lessons learned from Cyber Security Crime we will discuss how Edge and IoT hardware can be easy to exploit. We will then outline what security fundamentals are important to have in place along with common technology mitigations.

Join us while we take you through creating data science applications on the edge and how we can secure data, models and edge devices.

Bio: 

Audrey Reznik has been in the IT industry (private and public sectors) for 27 years in multiple verticals. In the last 4 years, she worked as a Data Scientist at ExxonMobil where she created a Data Science Enablement team to help data scientists easily deploy ML models in a Hybrid Cloud environment. Audrey was instrumental in educating scientists about what the OpenShift platform was and how to use OpenShift containers (images) to organize, run, and visualize data analysis results. Audrey now works as a Data Scientist with the Red Hat OpenShift Data Science Team where she is focused on next-generation applications. She is passionate about Data Science and in particular the current opportunities with ML and Federated Data.