Deploying and maintaining production-ready models

Deployment might seem like the very last step in a company’s journey towards becoming an AI-powered enterprise. They have already ensured the proper structure of their data, translated their business challenges into data science problems, and they have the perfect AI model ready to deploy and transform their business. 

But that’s not actually the end of an AI journey. In some ways, deployment is only the beginning.

To make an AI solution work for a business long-term, ongoing maintenance must be part of the deployment process. Without continued monitoring and adjustments, every model will eventually lose its precision, because it is no longer incorporating changes or growth in the data it uses. 

One clear example is in healthcare. AI models are now being used to interpret medical data like mammograms, which detect breast cancer. A model might be deployed using the most current medical data from a set of patients. But over the next year and beyond, more data becomes available about those patients: some remain healthy, some develop cancer, some go into remission, some suffer recurrences of previous disease, etc. If the model is not monitored and tuned to incorporate this new data, its precision deteriorates. 

This kind of complex monitoring and tuning is both absolutely necessary and quite difficult to accomplish. The best solution to the problem is a hybrid tool:

  1. A performance monitoring dashboard to monitor different aspects of the solution
  2. A team of human validators and tuners to apply necessary adjustments and validate their efficacy

This isn’t an easy combination to come by. Most companies lack the resources to maintain the number of skilled employees required to complete this sort of monitoring and tuning in-house.

This is where AI implementation companies like CrowdANALYTIX come in. CrowdANALYTIX leverages crowdsourcing not only to develop customized AI models, but also to monitor and tune them for long-term accuracy, which leads to long-term savings in costs and efficiencies.

The AI journey doesn’t end with deployment: monitoring has to be part of the package. 

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