Recent posts by Mona

Be the first to know about top trends within the AI / ML monitoring industry through Mona's blog. Read about our company and product updates.

Posts about AIMonitoring (5):

The virtuous cycle of AI-driven growth

The virtuous cycle of AI-driven growth

Making AI impactful and scalable is hard

In virtually every industry, companies invest heavily in artificial intelligence. We all have an intuitive understanding of the “why”: Within just a few years it has become much easier to build predictive models with real business value, that save time and money, reduce manual labor, and improve results across the board. Ultimately, we want AI to deliver business optimization and growth.  

The best gifts for data scientists

The best gifts for data scientists

Looking for a special gift for a data scientist or a data engineer at the office or in your life? You’ve come to the right place then. It’s just so hard to please them! We spent hours crunching data and running experiments with various gift ideas, narrowing the list to some of the best options so you won’t have to. Happy shopping!

The definitive guide to AI / ML monitoring

The definitive guide to AI / ML monitoring

 

Machine Learning Monitoring Overview 

Machine learning monitoring is a crucial aspect that allows you to develop insights into your ML models in production and ensure that they’re performing as intended. Monitoring is also a necessary precondition to extracting meaningful business value from your models. Without understanding how your model’s predictions are impacting downstream business KPIs and revenue, it’s impossible to make further improvements and optimizations to your modeling pipeline. ML monitoring ensures that you are able to take preemptive actions before small modeling problems turn into catastrophic, system-level failures.

Should you use ML monitoring solution offered by your cloud provider?

Should you use ML monitoring solution offered by your cloud provider?

As AI systems become increasingly ubiquitous in many industries, the need to monitor these systems rises. AI systems, much more than traditional software, are hypersensitive to changes in their data inputs. Consequently, a new class of AI monitoring solutions has risen at the data and functional level (rather than the infrastructure of application levels). These solutions aim to detect the unique issues that are common in AI systems, namely concept drifts, data drifts, biases, and more.