We are pleased to showcase our new interactive demo environment, which allows you to explore Mona’s dashboard and capabilities, free of charge and with 0 work! Mona’s mission has always been to make AI reliable and impactful, and we’re very excited to show data science teams the true value that Mona can bring to their day to day activities. We have built this interactive demo understanding that there are users out there that would prefer to try our platform for themselves, and this will allow you to do so without having to upload any data.
Posts about AIMonitoring:
Monitoring is crucial to ensuring that ML models deployed in production are serving their intended purpose and operating as expected, but how soon is too soon to implement a monitoring solution? Ultimately it depends on the extent to which the models are integrated into business processes, and this blog post will walk through the considerations that should be made before deciding to implement an ML monitoring solution.
Machine learning operations (MLOps) is currently one of the hottest areas for startup investment, because while best practices for building machine learning models are relatively well understood, a great deal of innovation is being poured into devising ways to best operationalize them for production. Chief among the MLOps categories is ML monitoring. Making sense of the landscape of ML monitoring tools can be frustrating, time consuming, and just plain confusing. Our goal with this article is to chart its cartography and, in doing so, hopefully illuminate some of the common pitfalls around choosing an appropriate monitoring solution, thereby bringing order to the chaos.
Trusting in artificial intelligence systems is not easy. Given the variety of edge cases on which machine learning models may fail, as well as the lack of visibility into the processes underlying their predictions and the difficulty of correlating their outputs to downstream business results, it’s no wonder that business leaders often look upon AI with some skepticism.