Editor's note: Today's guest post comes from AI for healthcare platform Lumiata. Here's the story of how they use Google Cloud to power their platform--performing data prepping, model building, and deployment to tackle inherent challenges in healthcare organizations. If ever there was a year for healthcare innovation--2020 was it. At Lumiata, we've been on a mission to deliver smarter, more cost-effective healthcare since 2013, but the COVID-19 pandemic added new urgency to our vision of making artificial intelligence (AI) easy and accessible. Using AI in healthcare went from a nice-to-have to a must-have for healthcare organizations.

With surmounting interest in data science and the fast-growing Data Scientist community, AI as a technology has come a long way crossing the chasm from Innovators and early adopters to the Early Majority. Along with all the hype that's there today around AI, there is still the unaddressed issue of less than 12% models reaching the production stage Data Scientists are creating models day in and day out but there are millions of models that are still waiting to see the light of the day in production. While the usual belief is that the deployment should need fewer days than building a model but it is becoming the most challenging issue of the industry today. Building the model is one thing, what's more, challenging is operationalizing AI. Analytics challenged leadership: This one serves as the major hurdle in operationalizing AI.

When AI practitioners talk about taking their machine learning models and deploying them into real-world environments, they don't call it deployment. Instead the term that's used is "operationalizing". This might be confusing for traditional IT operations managers and applications developers. Why don't we deploy or put into production AI models? What does AI operationalization mean and how is it different from the typical application development and IT systems deployment?

When AI practitioners talk about taking their machine learning models and deploying them into real-world environments, they don't call it deployment. Instead the term that's used is "operationalizing". This might be confusing for traditional IT operations managers and applications developers. Why don't we deploy or put into production AI models? What does AI operationalization mean and how is it different from the typical application development and IT systems deployment?