Life Sciences Cloud Hosting

To Succeed, First You Must Commit | #AnalyticsX

Life Sciences Cloud Hosting

I've spent the last couple of days at the Analytics Experience event in Milan, Italy. Milan is famous for high fashion of the couture kind, but there was plenty of fashionable advanced technology on show in the Milano Congressi (MiCo) this week. The man and woman in the street tends to assume that high fashion is not of relevance to them, but materials, manufacturing techniques and styles all percolate to the mass market. The same is true of advanced technology. Whilst some of the case stories were undoubtedly leading edge, very many were examples of real world problems that have been (fully or partially) solved by technology such as cloud adoption and artificial intelligence (AI). 

AI case studies that particularly appealed to me included the measurement of cancerous tumours in the liver (visual recognition), and the early prediction of failure in manufacturing processes from early diagnostic of vibration (event stream processing) .

However, it was also readily apparent that the speed with which many of these teams were able to operate and get their valuable results was greatly influenced by their use of cloud computing. Rather than be constrained to a single, shared compute resource, many of the speakers were making use of flexible, elastic, cloud based computational resources. And that cloud-based flexibility is being compounded by the use of containers to contain environments and allow greater agility in trying out new things and moving to newer versions sooner.

AI is moving from its teenage years to adult maturity. Increasing adoption of interpretability techniques like LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (Shapley Additive Explanation) helps provides trust and thus adoption with their ability to facilitate lineage and traceability from data to decision. However, the Gartner Hype Cycle for AI (2019) shows that we're not there yet, there a lot of AI technologies that are in the "trough of disillusionment"; only speech recognition and the use of GPU accelerators are rated by Gartner as having made their way to the Plateau of Productivity.

We very much see these trends at d-wise and are helping many clinical clients with the adoption of cloud, container and DevOps approaches and technologies, bringing them greater agility, efficiency and speed to solution.

Not many of our clients yet have a robust, industrialised approach to model building and deployment ("a model factory"), but the use of recommendation engines within user interfaces ("augmented intelligence") is an area that will begin to bear valuable fruit very soon.

Now is the time to challenge your packaged software providers to outline in their product roadmaps how they are incorporating AI to add business value in the form of advanced analytics, intelligent processes and advanced user experiences. To succeed with AI, first you must commit to the journey. And d-wise would be very glad to accompany you on your journey, providing our experience and knowledge to help you straighten and optimise the journey.