A new approach is required

“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


Industrializing AI with an end-to-end process

The value proposition of data science, 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.

43% of respondents to a survey completed by Algorithmia, cited scaling models as their biggest challenge, with versioning and reproducibility in ML models as the second greatest challenge at 41%.

ML Ops, 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 pipeline, with its products DataHUB4.0 and OPUS, through which users develop, deploy, monitor and maintain their AI models. This end-to-end automated process expedites the process to build models, using a no-code AI wizard. The platform is directly connected to the live operational environment with DataHub4.0, allowing models to be put into production automatically, removing the need to rely on personnel in a separate team, which results in the model creator losing control and visibility in most instances.

Models are monitored and maintained by their creator; however this process is streamlined, with models automatically refreshing with new data. Alerts can be set up if the model accuracy reduces and the model requires retaining, and a simple retrain process can be initiated in the platform.

Using an end-to-end automated AI pipeline, the industrialization of AI is here.

Download Whitepaper: Transitioning to Automated Data Science

End-to-end auto AI pipeline

OPUS No code AI diagram
Product Features

Why our customers choose VROC

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Unlimited AI Models

Train an unlimited number of AI models

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Automated Model deployment

Deploy your models directly into the live environment, retaining control

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Easy Model management

Easily monitor and manage thousands of AI models in one platform

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Detailed Predictions

Drill down into the direct root cause of your predictions

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Scaling enterprise wide

Overcome the skills gap with MLOps

The 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.

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Our client’s success is our success

“Most companies have struggled to address all the potential opportunities for data science in their business, because there are so many, and then they struggle to operationalize those models and make them into applications which users, engineers, maintainers and reliability engineers can make use of… The advent of autoML is trying to lift and remove some of the challenges of scaling and operationalizing models.”

Denis Marshment, previous Global Vice President – Data Science Customer Solutions, Worley

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Get started with VROC today

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.