Operational Excellence with Azure Machine Learning for Mining
Date and time
Location
Online event
Extract tangible value out of your operations data by using Machine Learning on the Azure platform
About this event
Please note: This webinar has been rescheduled from 11th of August to Thursday, 25th of August. We apologise for any inconvenience caused.
BizData and Tridiagonal, in partnership with Microsoft, invite you to take part in an interactive presentation where our experts will share insights into:
• How to start creating true value out of operations data by defining key problem statements and actionable objectives
• Which techniques to apply to each type of problem to get the best results
• How to select the right Machine Learning and Artificial Intelligence methods & workflows to reach your objectives
• How to build the practical solutions that will help you to extract value from your data
Machine Learning and Artificial Intelligence, together with other advanced modelling techniques, are continuously evolving and you can find thousands of algorithms, tools, platforms, etc., making it challenging to identify and validate the correct approach, technologies and solutions to use in the Mining industry. Furthermore, the success of a data analytics solution can only be realized if it can be readily deployed, managed and operated.
Join us to learn about our approach to:
1. Value Creation: a showcase of approaches for how to identify and classify the problem from a data analytics perspective.
2. Value Solution: a matrix of methods, approaches and solutions with use cases in the Mining industry.
3. Value Production: best practices to operationalise your models into dashboards, decision support systems, and other intelligent solutions.
In addition, we will share a live demonstration of the platform running on Azure, which will include:
1. ID fan predictive maintenance
2. % Silica concentrate prediction in the Iron Ore (Quality prediction in Mining)
3. Conveyor belt tension prediction
We look forward to seeing you there.