April 1, 2020 9:00 AM
April 1, 2020
3:00 - 4:00PM BST
4:00 - 5:00 BST+1
The focus of this webinar is to identify how Graph and Machine Learning can directly affect the high value use cases in Energy - Transmissions - Pipelines - Field based IoT - Networks. This event is designed as a 'Speed Dating' format with focus on key topics for under 15 minutes in order to maximize the insights. During this online meetup, you'll learn from our experts on how Expero and TigerGraph technology can unlock the potential in your organization. We will feature unique TigerGraph and Expero technology lightning talks, followed by a short Q&A. Four bite sized informative sessions to learn how to use TigerGraph and Expero to maximize the effectiveness of your business.
What You'll Learn:
- Energy and IoT connected systems - why SQL and current ML approaches fall short
- How utilizing Graph can cut costs by 10%
- Drive real-time & predictive decisions while increasing velocity
- Detailed review of ML methods and approaches combined with Graph to realize the accuracy increase
- Using Graph and ML to increase accuracy of Energy - Transmission - IoT determinations by 20%
Session 1 (12 Min): Why Graph and ML Visualizations are Game Changers - Energy Connected Systems
Session 2 (12 Min): 4 Different Pipelines, IoT Connected Demonstrations 'The Art of the Possible' - Demos of Drilling, IoT Energy, Pipelines, Transportation - IoT
Session 3 (12 Min): Details of How Graph & ML Increase Accuracy in Real-Time - How to Combine Graph & ML Algorithms for Predictive, Connected Networks
Session 4 (12 Min): Demo TigerGraph Combined with ML Approaches for Network Analytics and IoT Networks - Illustrate the Power of TigerGraph
Session 5 (5 min): Open Q&A
As Chief Revenue Officer, Scott is responsible for Expero's sales, marketing and strategic business development efforts. On an average day, you'll find him on the phone, at the whiteboard, or just gesticulating wildly in someone's office. Scott is skilled at distilling the Expero team's vast knowledge and experience into manageable, accessible and cohesive buckets.
Dr. G loves data. His favorite part of work is daydreaming up innovative solutions to quantifiable problems and planning an implementation strategy. Building intelligent systems is his passion whether it's automated derivatives trading bots, adaptive image processing algorithms, or autonomous musical composers. Whether deep learning is the optimal solution or not, helping customers succeed through solving their analytics problems is where Graham finds the most satisfaction.