Before you launch a project to build an artificial intelligence monitoring system from scratch, consider whether or not this would be a good use of your resources. When does it make sense to buy instead? Let’s discuss. This post explores the advantages and disadvantages of both alternatives so that you can make an informed decision about what’s best for your organization.
In the past 4 years I’ve been working with teams implementing automated workflows using ML, NLP, RPA, and many other techniques, for a myriad of business functions ranging from fraud detection, audio transcription all the way to satellite imagery classification. At various points in time, all of these teams realized that alongside the benefits of automation they have also added additional risk. They have lost their “eyes and ears on the field”, the natural oversight you get by having humans in the process.
Deploying AI can create immediate value — but sustaining and scaling that value is where the real challenge begins. Too often, teams ship models that perform well in a controlled environment but quickly lose effectiveness in the messy, ever-changing real world. Why? Because AI systems don’t operate in a vacuum. Data shifts, user behavior evolves, and business goals change.
As our customer base grows and the number of production AI use-cases being monitored by Mona increases, our team has been working tirelessly to advance our product to become a best in class AI observability solution.