The world of industrial analytics is becoming increasingly crowded with offerings from data science houses (consultancy) and data science programming tools (DIY), making it easy to become confused.
How do these offerings compare? Do they speed up the time to implement predictive maintenance and process optimisation? What are the advantages and disadvantages?
Think of these companies are like an off-site data science team – with a price tag! Yes, they build bespoke models of your assets, however there are a few things to watch out for;
With a great variety of tools on the market, some built for industry and some generic, these solutions empower your existing data scientist to model problems themselves. A couple of things worth noting;
At VROC we saw an opportunity to automate the above processes, providing AI predictive analytics for ‘whole of facility’ predictive maintenance and optimisation. This has been achieved through the development of an AI platform which doesn’t rely on pre-built models, nor programming skills and empowers subject matter experts and engineers by automating data science process.
Learn more about the differences between a traditional data science approach and an automated data science approach in our whitepaper ‘transitioning from traditional data science to automated data science’ – download it here
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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.
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