Recent posts by Mona

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Posts about AIMonitoring:

Data drift, concept drift, and how to monitor for them

Itai Bar Sinai, Co-founder and CPO May 2, 2022 1:37:32 PM
Data drift, concept drift, and how to monitor for them

Data and concept drift are frequently mentioned in the context of ML monitoring, but what exactly are they and how are they detected? Furthermore, given the common misconceptions surrounding them, are data and concept drift things to be avoided at all costs or natural and acceptable consequences of training models in production? Read on to find out. In this article we will provide a granular breakdown of data and concept drift, along with methods for detecting them and best practices for dealing with them when you do.

The Three Must-Haves for Machine Learning Monitoring

Mona Team Mar 22, 2022 1:12:44 PM
The Three Must-Haves for Machine Learning Monitoring

Monitoring is critical to the success of ML models deployed in production. 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 and deployment process in order to prevent model drift and predictive stasis. That said, not all monitoring solutions are created equal. These are the three must-haves for a machine learning monitoring tool, whether you are deciding to build or buy a solution. 

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

Itai Bar Sinai, Co-founder and CPO Dec 16, 2021 12:44:00 PM
The secret to successful 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. Now, if something goes wrong, there isn’t a human to notify them, and if there’s a place in which an improvement could be made, there might not be a human to think about it and recommend it. Put differently, they realized that humans weren’t only performing the task that is now automated, they were also there, at least partially, to monitor and QA the actual workflow. While each business function is different, and every automation or AI that is used has its own myriad of intricacies and things requiring monitoring and observing, one common thread binding all of these use-cases is that issues and opportunities for improvement usually appear in pockets, as opposed to grand sweeping, across the board.

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

Yotam Oren, Co-founder and CEO Nov 22, 2021 11:27:55 AM
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. 

Mona now offering free trial

Diane Vo Nov 2, 2021 3:42:23 PM
Mona now offering free trial

Here at Mona, we are now allowing new users to try our leading AI monitoring platform with a free 30 day trial! No credit card required, no strings attached. That’s right. You get instant access to our full platform including all features!