“Tying to scale machine learning models across thousands and thousands of failure modes in a plant is not practical in a traditional approach to data science. You can spend three to six months building a model, training it, testing it and then operationalising it, maybe longer”
Denis Marshment, previous Global Vice President - Data Science Customer Solutions, Worley
The value proposition of AI and machine learning is well established. The next challenge faced by businesses is how to scale these efficiently to realize the benefits more broadly. Most often, this comes down to challenges in scaling, versioning and reproducing ML models.
MLOps, also known as ML DevOps, is the application of processes and tools for the effective development, deployment, and monitoring of AI models.
VROC has automated the end-to-end AI/ML pipeline, solving the challenges of data wrangling and model scaling. Our solution automates ML and solves MLOps challenges, enabling users to easily develop, deploy, monitor and maintain their AI models.
The platform is directly connected to the live operational environment, for automated model production, removing the need to rely on separate teams, when often the model creator loses control and visibility. Models are monitored and maintained by their creator; however this process is streamlined, with models automatically refreshing with new data. Creators are alerted if the model accuracy reduces and the model requires retaining, and a simple retrain process can be initiated in the platform.
Deploy your models directly into the live environment, retaining control
Read MoreThe automated end-to-end AI pipeline reduces the reliance on a few highly skilled data science and analytics professionals. OPUS allows subject matter experts and engineers to rapidly build AI models with its no-code platform. These users can train, deploy, monitor, and manage their own models to gain business critical insights specific to their area of the business. This can be done without relying on other business departments, and limited personnel who are focused on other business priorities.
Using a single advanced analytics platform across an organisation, which a broad group of personnel can use, both helps overcome skills gaps and assists in the scaling of AI enterprise wide.
Ready to embark on a pilot project or roll-out the innovation enterprise wide? Perhaps you need assistance integrating your systems or accessing your data? We have a solution to help you as you progress through your digital transformation.
Interested in a demo of one of our data solution products?
DataHUB4.0 is our enterprise data historian solution, OPUS is our Auto AI platform and OASIS is our remote control solution for Smart Cities and Facilities.
Book your demo with our team today!
Ready to embark on a pilot project or roll-out AI innovation enterprise wide? Perhaps you need assistance integrating your systems or storing your big data? Whatever the situation, we are ready to help you on your digital transformation.
The efficient deployment, continuous retraining of models with live data and monitoring of model accuracy falls under the categorisation called MLOps. As businesses have hundreds and even.
Learn more about DataHUB+, VROC's enterprise data historian and visualization platform. Complete the form to download the product sheet.
Discover how you can connect disparate systems and smart innovations in one platform, and remotely control your smart facility. Complete the form to download the product sheet.
'OPUS, an artistic work, especially on a large scale'
Please complete the form to download the OPUS Product Sheet, and discover how you can scale Auto AI today.
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