Recent posts by Mona

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The three must haves for machine learning monitoring

The three must haves for machine learning monitoring

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.

The secret to successful AI monitoring: Get granular, but avoid noise

The secret to successful AI monitoring: Get granular, but avoid noise

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.

Continuous feedback is key to taking your AI from “good to great”

Continuous feedback is key to taking your AI from “good to great”

Deploying AI instantly brought value and growth to many businesses. However, it is well established that sustaining the value over time, not to mention maximizing it, could be quite challenging. Continuous optimization is the key to successful AI deployments. Beginning with a product that’s good enough, learning from how it performs in the real world, especially as the world (read: the data environment) changes, and then improving; then learning and improving again and so on. It’s a bit of an obvious insight but it is rare for AI-driven products to be perfect from day one.