Machine Learning Operations (ML Ops) & Internet Of Things (IOT)
Today's data landscape is complex – data comes at different speeds, in different formats, influencing different business decisions. Customers expect that organisations are actively responding to the information (data) they provide – whether it's from their connected devices, web activity or interaction with bricks and mortar. Reliance on automated decisions that directly impact customers – from recommendation engines, to real time BI reporting insights from IoT – are on the rise and require a slightly different skill set from the norm. Our team of ML Ops and IoT specialists bring their cutting edge knowledge of this fast developing field, to bring real time automated decisions from business aspiration to business reality.
Streaming Data
For data to be useful, it needs to be plentiful; but the more you have access to, the more unwieldy it can become, making it hard to understand and use. We are experienced in ingesting large amounts of data from sensors and devices, before turning it into actionable insights that can predict behaviours and warning signals you may need to react to. In short, we make data make sense, in order to guide business decisions. This delivers value in the form of reduced revenue loss, increased operational efficiency, identification of root-cause of issues and optimised productivity.
Unstructured Data
We are experienced in ingesting data including cutting edge techniques such as image analysis, computer vision, deep learning, motion & anomaly detection, text mining.
ML Ops
Moving to automated decisioning powered by machine learning needs the right infrastructure, the right approach to data engineering, the right governance over how machine learning models are applied, monitored and refreshed, the right application of business rules and the right connections to front-end UX. Our team of ML Ops go beyond the role of the normal data scientist and data engineers, to allow peace of mind to ensure the machines make the decisions when the lights go ‘off’.
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