Monitoring is critical to the success of machine learning models deployed in production systems. Because ML models are not static pieces of code but, rather, dynamic predictors which depend on data, hyperparameters, evaluation metrics, and many other variables, it is vital to have insight into the training, validation, deployment, and inference processes in order to prevent model drift and predictive stasis, and a host of additional issues. However, not all monitoring solutions are created equal. In this post, I highlight three must-haves for machine learning monitoring, which hopefully serve you well whether you are deciding to build or buy a solution.
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.
We hope that everyone had a fantastic holiday season and is now ready to tackle the 2022 New Year! Looking back to where we started in 2018 to where we are now, we have grown so much overall as a company. From three (⅓ balding) guys with a crazy idea nobody understood, through assembling a team of passionate trailblazers, and to building advanced features for Mona’s platform - now leveraged by incredible AI/ML teams at industry leaders, and even recognized by Gartner, we are continuing to strengthen our position as the leading monitoring solution for AI, - providing the most flexible and comprehensive insight engine.
In the past 3 years I’ve been working with teams implementing automated workflows using ML/DL, 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.