ML Monitoring Solution - Build vs. Buy Guide
Should you build your own machine learning monitoring solution or buy from a vendor? We break down the pros and cons of both options for ML monitoring.
There is no one-size-fits-all solution when it comes to monitoring ML models. For some, requirements may be so complex and unique that no tool on the market will satisfy their needs, so building a solution is their only choice. For others, monitoring needs are so minimal that they can rely on the minimal monitoring solutions provided by end-to-end ML platforms. For the rest, best-in-breed ML monitoring point solutions offer a cost-effective way to meet all of their monitoring requirements.
Rather than try to present a single recommendation for a one-size-fits-all solution, we’ve written this build vs. buy guide as a way to help our readers evaluate the best option for them. We’ve worked with dozens of companies who have gone through this process, some of whom opted to use the WhyLabs AI Observatory or our open source data logging library whylogs for their ML monitoring needs.
As teams across industries adopt AI, WhyLabs enables them to operate with certainty by providing model monitoring, preventing costly model failures, and facilitating cross-functional collaboration. Incubated at the Allen Institute for AI, WhyLabs is a privately-held, venture-funded company based in Seattle.