Enhance your operation with machine learning decision support. With proven success in pipeline deployment, we will help you achieve previously unimaginable and unattainable outcomes. Leverage our machine learning expertise and enterprise deployment to help you meet your goals.
The success of your ML project depends on three key areas of expertise:
Artificial Intelligence and SCADA
Artificial Intelligence (AI) integrated with SCADA is an aspirational trend we are seeing across multiple industries. Machine learning (ML) is a component of AI that is an excellent tool for identifying and enabling small improvements that deliver large gains in performance.
Many industries have been working to implement AI strategies to create or enhance decision support systems. However, there has been limited success. Gartner estimates that only 15% of the use cases leveraging AI techniques such as ML and involving Edge and IIoT environments will be successful over the next few years.
The number one reason why artificial intelligence projects fail is lack of expertise. MIT Sloan Management Review/Boston Consulting Group survey* concluded that “The gap between ambition and execution is large at most companies…companies lack analytics expertise.”
Three Key Areas
Three key areas of expertise are needed in a project team to build, train, and deploy machine learning models successfully. These areas are domain expertise, enterprise-grade deployment expertise, and ML expertise.
* MIT Sloan Management Review partnered with The Boston Consulting Group to conduct a global survey of more than 3,000 executives, managers, and analysts regarding implementing AI.
Are you as confident in ML and enterprise deployment as you are with your domain expertise?
Creating and Deploying a Successful Machine Learning Project
At Willowglen, we can bridge any of your gaps between domain knowledge, operational knowledge, and straight ML expertise. It’s only by bringing all three areas of expertise together, that an ML project will be successful.
A common obstacle for most operations trying to engage with ML is that companies that specialize in ML don’t know what parts of your data are relevant. There is a long period to educate the company about your business and industry – a time consuming and costly exercise – which often fails to deliver results after so much investment.
There are two benefits for your ML expert also having domain expertise. We know the physics, and we understand the complex and vast amount of data coming from the operations and the data historian. We understand the hardware components of the industries we serve (and even build some of our own!). This domain expertise means we know when to trust the data, and we know what to look for when analyzing it.
Machine Learning Expertise
Some organizations that recognize the gap between external ML companies and their lack of domain knowledge undertake the massive effort of building internal ML teams instead. This can certainly provide specialized results over a long period but carries major risks of cost and time overruns where you are liable. It also means you have to maintain that ML team long-term for enterprise support for the life of the solutions.
MIT Sloan Management Review has noted that the unavailability of labelled data is another challenge that stalls many of the machine learning projects. 227 North American companies undertaking enterprise ML projects were surveyed by Dimensional Research and 71% of teams reported that they ultimately outsource training data and other ML project activities.
The consensus is that labelling and training data sets is hard. Most companies need outside help of some kind. Due to our ML expertise and domain expertise we can accurately and efficiently label the data during the training phase – which is critical in creating supervised machine learning models. Those who have attempted ML projects in the past will realize that outsourcing the labelling task is a challenge as it requires both domain knowledge and ML expertise for it to be done to any desired effect.
Enterprise Deployment Experience
If your operation is mission-critical or has mission-critical elements, partnering with an ML provider with enterprise deployment experience is not a nice-to-have, it is a must. Enterprise deployment is the last piece needed for a successful ML project and is arguably the most important. Having enterprise deployment experience allows an ML project to be rolled out in a controllable and safe manner.
We are a software developer with hundreds of enterprise deployments in our portfolio. We are experts in redundancy, fallback, and we follow best practices in deployment.
How We Deploy Machine Learning Models
Deploying machine learning models is dependent on the risk tolerance of the organization and anticipating that comfort levels will change over time. There are three key ways to deploy a machine learning model ranging from decision support to full autonomous optimization:
Runs independently and provides recommendations for optimized operational parameters.
Allows operators to accept and execute recommendations on the screen.
Executes recommendations automatically
and notifies the operator.
Why Partner with Willowglen Systems for Machine Learning?
Willowglen’s mission continues to be to solve the world’s most difficult automation challenges. We specialize our machine learning efforts to help distributed mission-critical infrastructure to optimize their operations. Our depth of knowledge across a breadth of industries (pipeline, rail, electrical) gives us applicable foresight and practical system experience parallel to none.
50 Years of Expertise
We leverage our 50-year industrial SCADA expertise when developing our machine learning models. We understand domain specific data and incorporate our knowledge of physics, engineering, and fluid dynamics into our models. Our domain expertise in several industries (pipeline, rail, electrical) ensures that our value assessments and machine learning models are realistic, logical, and have a meaningful impact.
We have proven success with large-scale enterprise data management needs.