Q & A
EDITOR ’ S
To make decisions more quickly and accurately , enterprises are increasingly turning to Machine Learning , arguably today ’ s most practical application of Artificial Intelligence ( AI ). Machine Learning is a type of AI that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so . Machine Learning algorithms use historical data as input to predict new output values . Industry pundits share insights on how the channel can play an important role in helping their customers with Machine Learning deployments .
MIKE BROOKS , GLOBAL DIRECTOR ASSET PERFORMANCE MANAGEMENT , ASPEN TECHNOLOGY
HOW CAN THE CHANNEL HELP ENTERPRISES WITH MACHINE LEARNING DEPLOYMENTS IN THE MIDDLE EAST MARKET ?
It has been a journey for us for over 10 years ; where the early days involved the introduction of a new and little know technology into manufacturing where the staff saw nothing like it in college . As a result , it took a lot of education and proof trials . In the last five years or so , the knowledge of Machine Learning has leaned towards better acceptance due to the understanding of consumer applications from Google , Tesla , Amazon and others . Increasingly the proof points from successful implementations have led to growing adoption and scaling of initial implementations and channel partners have played a role in this success .
Our approach to the best suited verticals emanates from the businesses with the expensive equipment and very high-cost products . Consequently , we started oil and gas , heavy chemicals , mining , and pharmaceuticals . However , the approach with agents is completely agnostic to the types of equipment and industries . The approach we have taken needs only data and a little guidance . We approach equipment that spins , such as pumps and compressors , static equipment such as electrostatic precipitators and heat exchangers , and mobile equipment such as trains and trucks . Consequently , the market opens up to many more use cases to be addressed by our ML approach .
To help their customers succeed with Machine Learning , channel partners must engage a comprehensive approach to data quality with a process to examine all incoming data for eight assessments of data quality , such as missing data , out-ofrange data , frozen data , and complete data content .
The channel has a role to play in helping CIOs and IT leaders to first and most , select what the problem is . Don ’ t pick the most difficult problem to prove out the technology . Select a problem that ’ s been solved previously that clearly identifies the capabilities of the Machine Learning-based application not the Machine Learning . For example , for asset health problems , select a few pumps with proper data and that have shown failures in the past . Try a small project , generate success and progress to more equipment and more complex examples . Grow the success and the faith in the product . Additionally , pick the product with low-touch , ease-of-use , and scalability with a proven track record .
I believe one of the most important challenges is to select the right system your current staff can work with , knowing what they know now , and does not need additional technical and data services to implement initially and then to keep it working at full performance . It ’ s no good if the equipment models deteriorate as the process changes . Get one that is selflearning without external assistance from channel partners .
INTELLIGENT TECH CHANNELS 51