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The traditional approach, where you spend three to six months building a model, training it, testing it, and operationalising it, maybe longer, is not scalable if you want to achieve results.
- Denis Marshment, previous Global Vice President Data Science Customer Solutions, Worley
Traditional machine learning can take months, with weeks spent on ingesting and manually manipulating data. AutoML automates the time-consuming repetitive tasks in the machine learning process.
Unlike other automated ML products on the market, OPUS has an automated end to end pipeline, that includes data ingestion and pre-processing of structured or unstructured data, auto machine learning and ML-Operations.
OPUS’ model wizard allows users to build models in minutes that target specific outcomes such as,
Expedite your machine learning process from months to minutes with OPUS.Learn More About OPUS
Models can be built in minutes and automatically deployed straight into your live environment. No limitation on the number of models or data volume.Read More
Our simple step-by-step model wizard makes AI modelling easy, no programming or coding required.Read More
Drill-down insights highlight the root cause along with contributing factors and time to failure. Model confidence rating automatically calculated.Read More
No limit on the number of AI models that you can build. Test theories rapidly and scale rapidlyRead More
VROC OPUS allows you to focus on the insights that you can rapidly acquire, rather than the algorithm and the code. With rapid processing and high model accuracy levels, you can focus on the insights obtained to support the business decisions you need to make. The model’s by-products can be used to easily visualise the insights on dashboards, using the platforms inbuilt data visualisation features.
Once the model has been trained the user can automatically operationalize it. The insights will automatically refresh on the dashboard based on the live data which is ingested into DataHub4.0. Users can set alerts based on pre-defined thresholds, so they are always informed, anytime, anywhere.
“Trying to scale machine learning models across thousands and thousands of failure modes on a plant, is probably not practical in the traditional approach to data science, where you can spend three to six months building a model, training it testing and operationalising it, maybe longer.”
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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 DataHUB4.0, VROC's distributed enterprise data historian. Complete the form 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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